Awesome-Incremental-Learning Documentation

Awesome Incremental Learning

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Technical Documentation: xialeiliu/Awesome-Incremental-Learning

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Overview (`README.md`)

Awesome Incremental Learning / Lifelong learning

Survey

  • Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting (arXiv 2025) [paper][code]
  • Continual Learning With Knowledge Distillation: A Survey (TNNLS 2024) [paper]
  • Recent Advances of Multimodal Continual Learning: A Comprehensive Survey (arXiv 2024) [paper]
  • Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy (JBCS 2024) [paper]
  • Class-Incremental Learning: A Survey (TPAMI 2024) [paper][code]
  • Continual Learning with Pre-Trained Models: A Survey (IJCAI 2024) [paper][code]
  • Continual Learning of Large Language Models: A Comprehensive Survey (arXiv 2024) [paper][code]
  • A Comprehensive Survey of Continual Learning: Theory, Method and Application (TPAMI 2024) [paper]
  • A Comprehensive Empirical Evaluation on Online Continual Learning (ICCV Workshop 2023) [paper][code]
  • A Survey on Few-Shot Class-Incremental Learning (Neural Networks 2024) [paper]
  • A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning (Neural Networks 2023) [paper]
  • An Introduction to Lifelong Supervised Learning (arXiv 2022) [paper]
  • A Survey on Incremental Update for Neural Recommender Systems (arXiv 2023) [paper]
  • Continual Learning of Natural Language Processing Tasks: A Survey (arXiv 2022) [paper]
  • Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks (arXiv 2022) [paper]
  • Recent Advances of Continual Learning in Computer Vision: An Overview (arXiv 2021) [paper]
  • Replay in Deep Learning: Current Approaches and Missing Biological Elements (Neural Computation 2021) [paper]
  • Online Continual Learning in Image Classification: An Empirical Survey (Neurocomputing 2021) [paper] [code]
  • Continual Lifelong Learning in Natural Language Processing: A Survey (COLING 2020) [paper]
  • Class-incremental learning: survey and performance evaluation (TPAMI 2022) [paper] [code]
  • A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (Neural Networks) [paper] [code]
  • A continual learning survey: Defying forgetting in classification tasks (TPAMI 2021) [paper] [arxiv]
  • Continual Lifelong Learning with Neural Networks: A Review
    (Neural Networks) [paper]
  • Three scenarios for continual learning (Nature Machine Intelligence 2022) [paper][code]

Papers

2026

  • Is Parameter Isolation Better for Prompt-Based Continual Learning? (CVPR 2026) [paper]

  • Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning (CVPR 2026) [paper][code]

  • Semantic-Guided Global-Local Collaborative Prompt Learning for Few-Shot Class Incremental Learning (CVPR 2026) [paper]

  • Octopus: History-Free Gradient Orthogonalization for Continual Learning in Multimodal Large Language Models (CVPR 2026) [paper]

  • Elastic Weight Consolidation Done Right for Continual Learning (CVPR 2026) [paper][code]

  • Quantized Residuals to Continuous Prompts for Few-Shot Class Incremental Learning in Vision-Language Models (CVPR 2026) [paper]

  • On Token’s Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models (CVPR 2026) [paper][code]

  • Affordance-First Decomposition for Continual Learning in Video–Language Understanding (CVPR 2026) [paper]

  • Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment (CVPR 2026) [paper][code]

  • Seeing the Scene Matters: Revealing Forgetting in Video Understanding Models with a Scene-Aware Long-Video Benchmark (CVPR 2026) [paper]

  • Dual-Estimator: Decoupling Global and Local Semantic Shift for Drift Compensation in Class-Incremental Learning (CVPR 2026) [paper][code]

  • Multimodal Continual Instruction Tuning with Dynamic Gradient Guidance (CVPR 2026) [paper][[code](https:
    //github.com/lisongze/DGG)]

  • Continual Distillation of Teachers from Different Domains (CVPR 2026) [paper][code]

  • AdaPrior:Bayesian-InspiredAdaptivePriorCorrectionforLong-Tailed ContinualLearning (CVPR 2026) [paper]

  • Learning from Itself: Mining Internal Knowledge from Vision Language Models for Continual Learning (CVPR 2026) [paper][[code](https://github.com/
    jordangong/continual-learning)]

  • An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning (CVPR 2026) [paper]

  • The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models (CVPR 2026) [paper]

  • Parameter-efficient Continual Learning for Enhancing Plasticity without Forgetting under Limited Model Capacity (CVPR 2026) [paper]

  • HAD:Heterogeneity-Aware Distillation for Lifelong Heterogeneous Learning (CVPR 2026) [paper]

  • Beyond the Static-World: Lifelong Learning for All-in-One Medical Image Restoration (CVPR 2026) [paper]

  • Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective (CVPR 2026) [paper]

  • EW-DETR:Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer (CVPR 2026) [paper]

  • Representation-Steered Incremental Adapter-Tuning for Class-Incremental Learning with Pre-Trained Models (CVPR 2026) [paper][code]

  • Re-evaluating Continual VQA: Toward Fair and Robust Evaluation for Multimodal Continual Learning (CVPR 2026) [paper][code]

  • Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting (CVPR 2026) [paper][code]

  • CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning (CVPR 2026) [paper]

  • PACT: Phase-Like Transition Constraints in Adapter-Based Continual Learning of Vision-Language Models (CVPR 2026) [paper][code]

  • Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge (CVPR 2026) [paper][code]

  • TAPE: Task-Adaptive Prototype Evolution in Audio-Language Models for Fully Few-shot Class-incremental Audio Classification (CVPR 2026) [paper][code]

  • Beyond Myopic Alignment: Lookahead Optimization for Online Class-Incremental Learning (CVPR 2026) [paper]

  • SAME:Sparse and Anchored Model Editing for Heterogeneous Incremental Learning under Limited Data (CVPR 2026) [paper]

  • Beyond Prompt Degradation: Prototype-guided Dual-pool Prompting for Incremental Object Detection (CVPR 2026) [paper][code]

  • Decouple Your Discovery and Memory in Continual Generalized Category Discovery (CVPR 2026) [paper]

  • TAPE: Task-Adaptive Prototype Evolution in Audio-Language Models for Fully Few-shot Class-incremental Audio Classification (CVPR 2026) [paper][code]

  • Resolving the Stability-Plasticity Dilemma in Reinforcement Learning via Complementary Continual Critics (CVPR 2026) [paper]

  • Resolving the Stability-Plasticity Dilemma in Reinforcement Learning via Complementary Continual Critics (CVPR 2026) [paper]

  • CHEEM:Continual Learning by Reuse, New, Adapt and Skip A Hierarchical Exploration-Exploitation Approach (CVPR 2026) [paper][code]

  • A Faster Path to Continual Learning (CVPR 2026) [paper]

  • Beyond the Static World: Continual Category Discovery under Visual Drift (CVPR 2026) [paper]

  • DGS: Dual Gradient and Semantic-Shift Guided Low-Rank Adaptation for Class Incremental Learning (CVPR 2026) [paper]

  • Robustness Under Data Scarcity: Few-Shot Continual Adversarial Training for Evolving Threats (CVPR 2026) [paper][code]

  • Subspace Alignment for CLIP-based Continual Learning via Canonical Correlation Analysis (CVPR 2026) [paper] [code]

  • Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models (CVPR 2026) [paper]

  • Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning (CVPR 2026) [paper] [code]

  • Few-Shot Hybrid Incremental Learning: Continually Learning under Data Scarcity and Task Uncertainty (CVPR 2026) [paper]

  • HYCAL: ATraining-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning (CVPR 2026) [paper]

  • Geometry-driven OOD Detectors Are Class-Incremental Learners (CVPR 2026) [paper][code]

  • Dynamic Magic: Unleashing Restricted Knowledge for Lifelong Person Re-Identification (CVPR 2026) [paper]

  • Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models (CVPR 2026) [paper]

  • Incremental Object Detection via Future-Aware Decoupled Cross-Head Distillation (CVPR 2026) [paper]

  • Towards Dynamic Modality Alignment in Multimodal Continual Learning (CVPR 2026) [paper]

  • DK-DDIL: Adaptive Knowledge Retention for Dynamic Domain-Incremental Learning in Medical Imaging (CVPR 2026) [paper]

  • DSCA: Dynamic Subspace Concept Alignment for Lifelong VLM Editing (CVPR 2026) [paper]

  • BriMA: Bridged Modality Adaptation for Multi-Modal Continual Action Quality Assessment (CVPR 2026) [paper][code]

  • Ο•-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models (CVPR 2026) [paper]

  • InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training (CVPR 2026) [paper]

  • Spectral Mixture-of-Experts for Continual Learning (CVPR 2026) [paper][code]

  • One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning (ICLR 2026) [paper][code]

  • Detect, Decide, Unlearn: A Transfer-Aware Framework for Continual Learning (ICLR 2026) [paper]

  • Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning (ICLR 2026) [paper][code]

  • Continual Unlearning for Text-to-Image Diffusion Models: A Regularization Perspective (ICLR 2026) [paper][code]

  • Rethinking Continual Learning with Progressive Neural Collapse (ICLR 2026) [paper][code]

  • Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity (ICLR 2026) [paper][code]

  • MergeTune: Continued Fine-Tuning of Vision-Language Models (ICLR 2026) [paper][code]

  • IDER: IDempotent Experience Replay for Reliable Continual Learning (ICLR 2026) [paper][code]

  • Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models (ICLR 2026) [paper]

  • XIL: Cross-Expanding Incremental Learning (ICLR 2026) [paper]

  • Interference-Isolated Elastic Weight Consolidation and Knowledge Calibration for Incremental Object Detection (ICLR 2026) [paper]

  • Mapping Post-Training Forgetting in Language Models at Scale (ICLR 2026) [paper][code]

  • PRISM: Progressive Robust Learning for Open-World Continual Category Discovery (ICLR 2026) [paper]

  • Consistency-Driven Calibration and Matching for Few-Shot Class Incremental Learning (ICLR 2026) [paper][code]

  • Heads collapse, features stay: Why Replay needs big buffers (ICLR 2026) [paper]

  • PAC-Bayes bounds for cumulative loss in Continual Learning (ICLR 2026) [paper]

  • HippoTune: A Hippocampal Associative Loop–Inspired Fine-Tuning Method for Continual Learning (ICLR 2026) [paper][code]

  • Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning (ICLR 2026) [paper][code]

  • Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning (ICLR 2026) [paper][code]

  • Pi-CCA: Prompt-Invariant CCA Certificates for Replay-Free Continual Multimodal Learning (ICLR 2026) [paper]

  • The Lie of the Average: How Class Incremental Learning Evaluation Deceives You? (ICLR 2026) [paper][code]

  • CoMem: Compositional Concept-Graph Memory for Vision–Language Adaptation (ICLR 2026) [paper][code]

  • Quantized Gradient Projection for Memory-Efficient Continual Learning (ICLR 2026) [paper][code]

  • Reversible Primitive–Composition Alignment for Continual Vision–Language Learning (ICLR 2026) [paper]

  • RLAP-CLIP: Continual Multimodal Learning with Prototype Adaptation and Difficulty-Aware Routing (ICLR 2026) [paper]

  • KeepLoRA: Continual Learning with Residual Gradient Adaptation (ICLR 2026) [paper][code]

  • Asymmetric Synthetic Data Update for Domain Incremental Dataset Distillation (ICLR 2026) [paper]

  • LCA: Local Classifier Alignment for Continual Learning (ICLR 2026) [paper]

  • Revisiting Weight Regularization for Low-Rank Continual Learning (ICLR 2026) [paper][code]

  • Point-UQ: An Uncertainty-Quantification Paradigm for Point Cloud Few-Shot Class Incremental Learning (ICLR 2026) [paper]

  • Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning (ICLR 2026) [paper][code]

  • Plug-and-Play Compositionality for Boosting Continual Learning with Foundation Models (ICLR 2026) [paper][code]

  • Enhanced Continual Learning of Vision-Language Models with Model Fusion (ICLR 2026) [paper][code]

  • Naming to Learn: Class Incremental Learning for Vision-Language Model with Unlabeled Data (ICLR 2026) [paper][code]

  • StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning (ICLR 2026) [paper]

  • SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting (ICLR 2026) [paper][code]

  • Merge before Forget: A Single LoRA Continual Learning via Continual Merging (ICLR 2026) [paper]

  • Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning (ICLR 2026) [paper][code]

  • Understanding the Dynamics of Forgetting and Generalization in Continual Learning via the Neural Tangent Kernel (ICLR 2026) [paper]

  • Activation Function Design Sustains Plasticity in Continual Learning (ICLR 2026) [paper]

  • FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning (ICLR 2026) [paper][code]

  • Active Continual Learning with Metaplastic Binary Bayesian Neural Networks (ICML 2026) [paper][code]

  • Advancing Analytic Class-Incremental Learning through Vision-Language Calibration (ICML 2026) [paper][code]

  • Adversarial Latent Embedding Repair for LLM Continual Learning (ICML 2026) [paper]

  • AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning (ICML 2026) [paper][code]

  • Beyond Buffer Limits: Energy-Based Data Reassembly for Continual Learning (ICML 2026) [paper]

  • Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning (ICML 2026) [paper][code]

  • Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning (ICML 2026) [paper]

  • BTSP-CAM: A Brain-Inspired Geometric Memory for Class-Incremental Learning (ICML 2026) [paper]

  • Calibrated Knowledge Aggregation in Bayesian Mixture-of-Experts for Continual VQA (ICML 2026) [paper]

  • Capacity-Agnostic Parameter Isolation for Continual Graph Learning (ICML 2026) [paper]

  • CE$^4$L: Continual Ego, Exo, and Ego-Exo Learning (ICML 2026) [paper][code]

  • Continual Learning of Domain-Invariant Representations (ICML 2026) [paper]

  • Continual Segmentation under Joint Nonstationarity (ICML 2026) [paper][code]

  • CoPE: Continual Probe-guided Expansion for Large Vision-Language Models (ICML 2026) [paper]

  • DiL: Discrete-anchored Representation Alignment for Semi-Supervised Continual Learning (ICML 2026) [paper]

  • Don't Forget Why You Started: Tackling Dual Forgetting in Vision-Language Continual Learning (ICML 2026) [paper]

  • ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation. (ICML 2026) [paper][code]

  • Energy-Structured Low-Rank Adaptation for Continual Learning (ICML 2026) [paper]

  • Expert Routing with Synthetic Data for Domain Incremental Learning (ICML 2026) [paper]

  • ExSkill: Continual Learning from Experience and Skills in Multimodal Agents (ICML 2026) [paper][code]

  • Factor-Wise Homogeneity of Slot-Attention for Continual Object-Centric Learning (ICML 2026) [paper][code]

  • Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection (ICML 2026) [paper]

  • GR-LoRA: Gradient-Recycling Low-Rank Adaptation for Class-Incremental Learning (ICML 2026) [paper][code]

  • HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning (ICML 2026) [paper][code]

  • Hierarchical Filtering and Refinement Classification for Few-Shot Class-Incremental Learning (ICML 2026) [paper][code]

  • HypCL: Adapting CLIP in Hyperbolic Space for Continual Learning (ICML 2026) [paper]

  • Hyper-LLaVA: Hyperbolic Uncertainty-aware Modality-Balanced Routing for Multimodal Continual Instruction Tuning (ICML 2026) [paper]

  • Hyperbolic Multimodal Continual Learning (ICML 2026) [paper]

  • JANUS-LORA: A Balanced Low-Rank Adaptation for Continual Learning (ICML 2026) [paper]

  • Learnability-Driven Knowledge Assimilation for Class-Incremental Semantic Segmentation (ICML 2026) [paper]

  • Little By Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts (ICML 2026) [paper][code]

  • MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery (ICML 2026) [paper][code]

  • MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning (ICML 2026) [paper][code]

  • Mitigating Plasticity Loss through Architectural Design in Continual Learning (ICML 2026) [paper]

  • Mixing Expertise with Confidence: A Mixture of Experts Framework for Robust Multi-Modal Continual Learning (ICML 2026) [paper]

  • MoCL: Metabolic Optimization for Curvature-Aware Continual Learning (ICML 2026) [paper]

  • On the Power of Statistics in Class-Incremental Learning with Pretrained Models (ICML 2026) [paper]

  • On the Theory of Continual Learning with Gradient Descent for Neural Networks (ICML 2026) [paper]

  • Online Continual Learning with Dynamic Label Hierarchies (ICML 2026) [paper][code]

  • Parameter-Masked Decoupled Optimization for Cross-Domain Class-Incremental Learning (ICML 2026) [paper]

  • PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning (ICML 2026) [paper][code]

  • Preserving Plasticity in Continual Learning via Dynamical Isometry (ICML 2026) [paper]

  • Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the Past (ICML 2026) [paper][code]

  • Retrospective Feature Estimation for Continual Learning (ICML 2026) [paper][code]

  • SABER: Continual Learning with Representation Conflict Management (ICML 2026) [paper]

  • SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning (ICML 2026) [paper][code]

  • SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport (ICML 2026) [paper]

  • Scaling Continual Learning with Bi-Level Routing Mixture-of-Experts (ICML 2026) [paper][code]

  • Self-Distillation Enables Continual Learning (ICML 2026) [paper][code]

  • Shapley Neuron Values for Continual Learning: Which Neurons Matter Most? (ICML 2026) [paper]

  • Skill Neologisms: Towards Skill-based Continual Learning (ICML 2026) [paper][code]

  • Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning (ICML 2026) [paper]

  • Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation (ICML 2026) [paper][code]

  • Subspace-Aware Feature Reshaping for Open-Set Graph Class-Incremental Learning (ICML 2026) [paper]

  • Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection (ICML 2026) [paper]

  • Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning (ICML 2026) [paper][code]

  • Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm (ICML 2026) [paper][code]

  • Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning (ICML 2026) [paper][code]

2025

  • DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection (NeurIPS 2025) [paper][code]

  • Policy Compatible Skill Incremental Learning via Lazy Learning Interface (NeurIPS 2025) [paper][code]

  • Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning (NeurIPS 2025) [paper]

  • Gated Integration of Low-Rank Adaptation for Continual Learning of Large Language Models (NeurIPS 2025) [paper]

  • MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging (NeurIPS 2025) [paper][code]

  • Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning (NeurIPS 2025) [paper][code]

  • Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning (NeurIPS 2025) [paper][code]

  • MemEIC: A Step Toward Continual and Compositional Knowledge Editing (NeurIPS 2025) [paper][code]

  • C-NAV: Towards Self-Evolving Continual Object Navigation in Open World (NeurIPS 2025) [paper][code]

  • Hybrid Re-matching for Continual Learning with Parameter-Efficient Tuning (NeurIPS 2025) [paper][code]

  • A Minimalistic Unified Framework for Incremental Learning across Image Restoration Tasks (NeurIPS 2025) [paper]

  • REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning (NeurIPS 2025) [paper]

  • Learning Multi-Source and Robust Representations for Continual Learning (NeurIPS 2025) [paper][code]

  • Learning Expandable and Adaptable Representations for Continual Learning (NeurIPS 2025) [paper][code]

  • Dynamic Siamese Expansion Framework for Improving Robustness in Online Continual Learning (NeurIPS 2025) [paper]

  • Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning (NeurIPS 2025) [paper][code]

  • C2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning (NeurIPS 2025) [paper]

  • Bisecle: Binding and Separation in Continual Learning for Video Language Understanding (NeurIPS 2025) [paper]

  • Mixture of Noise for Pre-Trained Model-Based Class-Incremental Learning (NeurIPS 2025) [paper][code]

  • Continual Multimodal Contrastive Learning (NeurIPS 2025) [paper][code]

  • Class-wise Balancing Data Replay for Federated Class-Incremental Learning (NeurIPS 2025) [paper]

  • Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning (NeurIPS 2025) [paper][code]

  • Separating the 'what' and 'how' of compositional computation to enable reuse and continual learning (NeurIPS 2025) [paper]

  • Demystifying Language Model Forgetting with Low-rank Example Associations (NeurIPS 2025) [paper]

  • Resource-Constrained Federated Continual Learning: What Does Matter? (NeurIPS 2025) [paper]

  • GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation (NeurIPS 2025) [paper]

  • Turning the Tables: Enabling Backward Transfer via Causal-Aware LoRA in Continual Learning (NeurIPS 2025) [paper]

  • Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models (NeurIPS 2025) [paper][code]

  • AnaCP: Toward Upper-Bound Continual Learning via Analytic Contrastive Projection (NeurIPS 2025) [paper]

  • Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning (NeurIPS 2025) [paper]

  • Gradient-Guided Epsilon Constraint Method for Online Continual Learning (NeurIPS 2025) [paper]

  • Temporal-Difference Variational Continual Learning (NeurIPS 2025) [paper]

  • Confusion-Driven Self-Supervised Progressively Weighted Ensemble Learning for Non-Exemplar Class Incremental Learning (NeurIPS 2025) [paper]

  • Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddings (NeurIPS 2025) [paper][code]

  • MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs (NeurIPS 2025) [paper]

  • Self-Evolving Pseudo-Rehearsal for Catastrophic Forgetting with Task Similarity in LLMs (NeurIPS 2025) [paper][code]

  • Continual Gaussian Mixture Distribution Modeling for Class Incremental Semantic Segmentation (NeurIPS 2025) [paper]

  • Continuous Subspace Optimization for Continual Learning (NeurIPS 2025) [paper]

  • Dual-Space Semantic Synergy Distillation for Continual Learning of Unlabeled Streams (NeurIPS 2025) [paper]

  • Class-wise Balancing Data Replay for Federated Class-Incremental Learning (NeurIPS 2025) [paper]

  • Continual Knowledge Adaptation for Reinforcement Learning (NeurIPS 2025) [paper][code]

  • Learn and Ensemble Bridge Adapters for Multi-domain Task Incremental Learning (NeurIPS 2025) [paper]

  • Merging on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging (NeurIPS 2025) [paper][code]

  • Federated Continual Learning via Orchestrating Multi-Scale Expertise (NeurIPS 2025) [paper]

  • Reliable Lifelong Multimodal Editing: Conflict-Aware Retrieval Meets Multi-Level Guidance (NeurIPS 2025) [paper]

  • Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning (NeurIPS 2025) [paper]

  • Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL (NeurIPS 2025) [paper]

  • Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity (NeurIPS 2025) [paper]

  • Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning (ICCV 2025) [paper][code]

  • Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning (ICCV 2025) [paper][code]

  • Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning (ICCV 2025) [paper][code]

  • RainbowPrompt: Diversity-Enhanced Prompt-Evolving for Continual Learning (ICCV 2025) [paper]

  • NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments (ICCV 2025) [paper][code]

  • VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting (ICCV 2025) [paper][code]

  • FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning (ICCV 2025) [paper][code]

  • SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning (ICCV 2025) [paper][code]

  • PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning (ICCV 2025) [paper][code]

  • CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization (ICCV 2025) [paper][code]

  • DMNSP: Dynamic Multi-Layer Null Space Projection for Vision-Language Continual Learning (ICCV 2025) [paper][code]

  • Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering (ICCV 2025) [paper][code]

  • Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning (ICCV 2025) [paper][code]

  • TWIST&SCOUT:GroundingMultimodal LLM-Experts by Forget-Free Tuning (ICCV 2025) [paper]

  • Adversarial Robust Memory-Based Continual Learner (ICCV 2025) [paper]

  • CRAM:Large-scale Video Continual Learning with Bootstrapped Compression (ICCV 2025) [paper]

  • iManip: Skill-Incremental Learning for Robotic Manipulation (ICCV 2025) [paper]

  • Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation (ICCV 2025) [paper][code]

  • DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic (ICCV 2025) [paper]

  • Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification (ICCV 2025) [paper][code]

  • Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning (ICCV 2025) [paper][code]

  • Integrating Task-Specific and Universal Adapters for Pre-Trained Model-based Class-Incremental Learning (ICCV 2025) [paper][code]

  • Joint Diffusion Models in Continual Learning (ICCV 2025) [paper]

  • External Knowledge Injection for CLIP-Based Class-Incremental Learning (ICCV 2025) [paper][code]

  • Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning (ICCV 2025) [paper][code]

  • Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models (ICCV 2025) [paper]

  • CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning (ICCV 2025) [paper][code]

  • Dual Domain Control via Active Learning for Remote Sensing Domain Incremental Object Detection (ICCV 2025) [paper]

  • Feature Decomposition-Recomposition in Large Vision-Language Model for Few-Shot Class-Incremental Learning (ICCV 2025) [paper]

  • Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification (ICCV 2025) [paper]

  • PLAN: Proactive Low-Rank Allocation for Continual Learning (ICCV 2025) [paper]

  • Overcoming Dual Drift for Continual Long-Tailed Visual Question Answering (ICCV 2025) [paper]

  • Continual Personalization for Diffusion Models (ICCV 2025) [paper]

  • Task-Aware Prompt Gradient Projection for Parameter-Efficient Tuning Federated Class-Incremental Learning (ICCV 2025) [paper]

  • Lark: Low-Rank Updates After Knowledge Localization for Few-shot Class-Incremental Learning (ICCV 2025) [paper]

  • CoSMIC: Continual Self-supervised Learning for Multi-Domain Medical Imaging via Conditional Mutual Information Maximization (ICCV 2025) [paper]

  • Flexi-FSCIL: Adaptive Knowledge Retention for Breaking the Stability-Plasticity Dilemma in Few-Shot Class-Incremental Learning (ICCV 2025) [paper]

  • Generalization-Preserved Learning: Closing the Backdoor to Catastrophic Forgetting in Continual Deepfake Detection (ICCV 2025) [paper]

  • A Tiny Change, A Giant Leap: Long-Tailed Class-Incremental Learning via Geometric Prototype Alignment (ICCV 2025) [paper]

  • Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models (ACL 2025) [paper]

  • Serial Lifelong Editing via Mixture of Knowledge Expert (ACL 2025) [paper]

  • Efficient Domain Continual pretraining by Mitigating the Stability Gap (ACL 2025) [paper]

  • Neuron-Level Sequential Editing for Large Language Models (ACL 2025) [paper][code]

  • Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (ACL 2025) [paper][code]

  • HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model (ACL 2025) [paper][code]

  • Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (ACL 2025) [paper]

  • Continual Gradient Low-Rank Projection Fine-Tuning for LLMs (ACL 2025) [paper][code]

  • A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning (ACL 2025) [paper][code]

  • Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory (ACL 2025) [paper][code]

  • Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning (ACL 2025) [paper]

  • Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (ACL 2025) [paper][code]

  • TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining (ACL 2025) [paper][code]

  • Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models (ICML 2025) [paper][code]

  • Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks (ICML 2025) [paper]

  • Predicting the Susceptibility of Examples to Catastrophic Forgetting (ICML 2025) [paper]

  • Tensor Decomposition Based Memory-Efficient Incremental Learning (ICML 2025) [paper]

  • Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning (ICML 2025) [paper][code]

  • Componential Prompt-Knowledge Alignment for Domain Incremental Learning (ICML 2025) [paper][code]

  • LADA: Scalable Label-Specific CLIP Adapter for Continual Learning (ICML 2025) [paper][code]

  • Reinforced Lifelong Editing for Language Models (ICML 2025) [paper][code]

  • Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning (ICML 2025) [paper][code]

  • Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts (ICML 2025) [paper]

  • Unlocking the Power of Rehearsal in Continual Learning: ATheoretical Perspective (ICML 2025) [paper]

  • Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting (ICML 2025) [paper]

  • Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn (ICML 2025) [paper][code]

  • A Selective Learning Method for Temporal Graph Continual Learning (ICML 2025) [paper]

  • Continual Reinforcement Learning by Planning with Online World Models (ICML 2025) [paper]

  • Knowledge Retention in Continual Model-Based Reinforcement Learning (ICML 2025) [paper]

  • SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning (ICML 2025) [paper][code]

  • Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector (ICML 2025) [paper][code]

  • Understanding the Limits of Lifelong Knowledge Editing in LLMs (ICML 2025) [paper][code]

  • Knowledge Swapping via Learning and Unlearning (ICML 2025) [paper][code]

  • BECAME: Bayesian Continual Learning with Adaptive Model Merging (ICML 2025) [paper][code]

  • L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning (ICML 2025) [paper][code]

  • Optimal Task Order for Continual Learning of Multiple Tasks (ICML 2025) [paper]

  • The Importance of Being Lazy: Scaling Limits of Continual Learning (ICML 2025) [paper]

  • Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers (ICML 2025) [paper]

  • Learning Dynamics in Continual Pre-Training for Large Language Models (ICML 2025) [paper]

  • ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think (ICML 2025) [paper]

  • Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters (ICML 2025) [paper]

  • Model Uncertainty Quantification by Conformal Prediction in Continual Learning (ICML 2025) [paper]

  • Learning without Isolation: Pathway Protection for Continual Learning (ICML 2025) [paper][code]

  • iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection (ICML 2025) [paper][code]

  • Online Curvature-Aware Replay: Leveraging $\mathbf{2^{nd}}$ Order Information for Online Continual Learning (ICML 2025) [paper]

  • CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning (ICML 2025) [paper][code]

  • Adaptive Localization of Knowledge Negation for Continual LLM Unlearning (ICML 2025) [paper][code]

  • From RAG to Memory: Non-Parametric Continual Learning for Large Language Models (ICML 2025) [paper][code]

  • FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence (ICML 2025) [paper]

  • Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning (ICML 2025) [paper][code]

  • Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective (ICML 2025) [paper]

  • Where is the Truth? The Risk of Getting Confounded in a Continual World (ICML 2025) [paper][code]

  • Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning (ICML 2025) [paper][code]

  • Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective (ICML 2025) [paper][code]

  • Autoencoder-Based Hybrid Replay for Class-Incremental Learning (ICML 2025) [paper]

  • Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning (ICML 2025) [paper][code]

  • Large Continual Instruction Assistant (ICML 2025) [paper][code]

  • TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree (ICML 2025) [paper]

  • Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting (ICML 2025) [paper]

  • Prototype-Based Continual Learning with Label-free Replay Buffer and Cluster Preservation Loss (CVPR Workshop 2025) [paper]

  • Prototype Augmented Hypernetworks for Continual Learning (CVPR Workshop 2025) [paper]

  • Language Guided Concept Bottleneck Models for Interpretable Continual Learning (CVPR 2025) [paper][code]

  • Dynamic Integration of Task-Specific Adapters for Class Incremental Learning (CVPR 2025) [paper]

  • Towards Continual Universal Segmentation (CVPR 2025) [paper]

  • Activating Sparse Part Concepts for 3D Class Incremental Learning (CVPR 2025) [paper][code]

  • Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation (CVPR 2025) [paper]

  • CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning (CVPR 2025) [paper][code]

  • CoMBO: Conflict Mitigation via Branched Optimization for Class Incremental Segmentation (CVPR 2025) [paper][code]

  • BiLoRA: Almost-orthogonal Parameter Spaces for Continual Learning (CVPR 2025) [paper][code]

  • Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning (CVPR 2025) [paper]

  • Incremental Object Keypoint Learning (CVPR 2025) [paper]

  • Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization (CVPR 2025) [paper][code]

  • AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation (CVPR 2025) [paper]

  • SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning (CVPR 2025) [paper]

  • Knowledge Memorization and Rumination for Pre-trained Model-based Class-Incremental Learning (CVPR 2025) [paper][code]

  • Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging (CVPR 2025) [paper]

  • Revisiting Generative Replay for Class Incremental Object Detection (CVPR 2025) [paper][code]

  • Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning (CVPR 2025) [paper][code]

  • Online Task-Free Continual Learning via Dynamic Expansionable Memory Distribution (CVPR 2025) [paper][code]

  • H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detection (CVPR 2025) [paper][code]

  • KAC: Kolmogorov-Arnold Classifier for Continual Learning (CVPR 2025) [paper][code]

  • FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding (CVPR 2025) [paper]

  • Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection (CVPR 2025) [paper][code]

  • T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning (CVPR 2025) [paper]

  • LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning (CVPR 2025) [paper][code]

  • Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning (CVPR 2025) [paper]

  • Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling (CVPR 2025) [paper]

  • Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning (CVPR 2025) [paper][code]

  • Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration (CVPR 2025) [paper][code]

  • Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds (CVPR 2025) [paper][code]

  • ProtoDepth: Unsupervised Continual Depth Completion with Prototypes (CVPR 2025) [paper][code]

  • Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation (CVPR 2025) [paper][code]

  • Do Your Best and Get Enough Rest for Continual Learning (CVPR 2025) [paper][code]

  • Task-Agnostic Guided Feature Expansion for Class-Incremental Learning (CVPR 2025) [paper][code]

  • ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation (CVPR 2025) [paper]

  • Synthetic Data is an Elegant GIFT for Continual Vision-Language Models (CVPR 2025) [paper][code]

  • Rethinking Query-based Transformer for Continual Image Segmentation (CVPR 2025) [paper][code]

  • Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need (CVPR 2025) [paper][code]

  • Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping (CVPR 2025) [paper][code]

  • Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning (CVPR 2025) [paper][code]

  • AVQACL: A Novel Benchmark for Audio-Visual Question Answering Continual Learning (CVPR 2025) [paper][code]

  • Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor (CVPR 2025) [paper][code]

  • One-for-More: Continual Diffusion Model for Anomaly Detection (CVPR 2025) [paper][code]

  • Learning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning (CVPR 2025) [paper][code]

  • Adaptive Retention & Correction: Test-Time Training for Continual Learning (ICLR 2025) [paper][code]

  • Prototype antithesis for biological few-shot class-incremental learning (ICLR 2025) [paper]

  • Coreset Selection via Reducible Loss in Continual Learning (ICLR 2025) [paper][code]

  • LOIRE: LifelOng learning on Incremental data via pre-trained language model gRowth Efficiently (ICLR 2025) [paper]

  • Active Learning for Continual Learning: Keeping the Past Alive in the Present (ICLR 2025) [paper]

  • TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models (ICLR 2025) [paper][code]

  • On Large Language Model Continual Unlearning (ICLR 2025) [paper][code]

  • SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning (ICLR 2025) [paper][code]

  • Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting (ICLR 2025) [paper]

  • ADAPT: Attentive Self-Distillation and Dual-Decoder Prediction Fusion for Continual Panoptic Segmentation (ICLR 2025) [paper][code]

  • Semantic Aware Representation Learning for Lifelong Learning (ICLR 2025) [paper][code]

  • Spurious Forgetting in Continual Learning of Language Models (ICLR 2025) [paper][code]

  • CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models (ICLR 2025) [paper][code]

  • Theory on Mixture-of-Experts in Continual Learning (ICLR 2025) [paper]

  • Boosting Multiple Views for pretrained-based Continual Learning (ICLR 2025) [paper]

  • STAR: Stability-Inducing Weight Perturbation for Continual Learning (ICLR 2025) [paper][code]

  • Optimal Protocols for Continual Learning via Statistical Physics and Control Theory (ICLR 2025) [paper]

  • Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification (ICLR 2025) [paper]

  • Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning (ICLR 2025) [paper][code]

  • A Second-Order Perspective on Model Compositionality and Incremental Learning (ICLR 2025) [paper][code]

  • C-CLIP: Multimodal Continual Learning for Vision-Language Model (ICLR 2025) [paper][code]

  • Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline (ICLR 2025) [paper][code]

  • Adapt-$\infty$: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection (ICLR 2025) [paper][code]

  • Learning Continually by Spectral Regularization (ICLR 2025) [paper]

  • Self-Normalized Resets for Plasticity in Continual Learning (ICLR 2025) [paper][code]

  • PseDet: Revisiting the Power of Pseudo Label in Incremental Object Detection (ICLR 2025) [paper][code]

  • Meta-Continual Learning of Neural Fields (ICLR 2025) [paper][code]

  • Vision and Language Synergy for Rehearsal Free Continual Learning (ICLR 2025) [paper][code]

  • Advancing Prompt-based Methods for Replay-Independent General Continual Learning (ICLR 2025) [paper][code]

  • LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning (CVPR2025) [paper][code]

  • KAC: Kolmogorov-Arnold Classifier for Continual Learning (CVPR2025) [paper][code]

  • HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning (TPAMI 2025) [paper]

  • Adaptive Score Alignment Learning for Continual Perceptual Quality Assessment of 360-Degree Videos in Virtual Reality (VR-TVCG 2025) [paper][code]

2024

  • Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning (ICPR2024) [paper][code]

  • CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning (BMVC24)[paper][code]

  • Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning (ICASSP2024) [paper] [code]

  • Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning (AAAI2024)[paper]

  • Maintaining Fairness in Logit-based Knowledge Distillation for Class-Incremental Learning (AAAI 2024) [paper][code]

  • Stabilizing Zero-Shot Prediction: A Novel Antidote to Forgetting in Continual Vision-Language Tasks (NeurIPS 2024) [paper][code]

  • GACL: Exemplar-Free Generalized Analytic Continual Learning (NeurIPS 2024) [paper][code]

  • Continual Audio-Visual Sound Separation (NeurIPS 2024) [paper][code]

  • F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning (NeurIPS 2024) [paper][code]

  • Adaptive Visual Scene Understanding: Incremental Scene Graph Generation (NeurIPS 2024) [paper][code]

  • Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings (NeurIPS 2024) [paper]

  • Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning (NeurIPS 2024) [paper][code]

  • Continual Learning in the Frequency Domain (NeurIPS 2024) [paper][code]

  • Saliency-driven Experience Replay for Continual Learning (NeurIPS 2024) [paper][code]

  • PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding (NeurIPS 2024) [paper][code]

  • Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models (NeurIPS 2024) [paper][code]

  • Continual Learning with Global Alignment (NeurIPS 2024) [paper]

  • Disentangling and mitigating the impact of task similarity for continual learning (NeurIPS 2024) [paper]

  • Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach (NeurIPS 2024) [paper][code]

  • Make Continual Learning Stronger via C-Flat (NeurIPS 2024) [paper][code]

  • ViLCo-Bench: VIdeo Language COntinual learning Benchmark (NeurIPS 2024) [paper][code]

  • Not Just Object, But State: Compositional Incremental Learning without Forgetting (NeurIPS 2024) [paper][code]

  • Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning (NeurIPS 2024) [paper][code]

  • Random Representations Outperform Online Continually Learned Representations (NeurIPS 2024) [paper][code]

  • Mixture of Experts Meets Prompt-Based Continual Learning (NeurIPS 2024) [paper][code]

  • SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models (NeurIPS 2024) [paper][code]

  • Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning (NeurIPS 2024) [paper][code]

  • Persistence Homology Distillation for Semi-supervised Continual Learning (NeurIPS 2024) [paper][code]

  • Continual learning with the neural tangent ensemble (NeurIPS 2024) [paper]

  • Vector Quantization Prompting for Continual Learning (NeurIPS 2024) [paper][code]

  • CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models (NeurIPS 2024) [paper][code]

  • An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning (NeurIPS 2024) [paper][code]

  • Label Delay in Online Continual Learning (NeurIPS 2024) [paper][code]

  • Visual Prompt Tuning in Null Space for Continual Learning (NeurIPS 2024) [paper][code]

  • Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation (NeurIPS 2024) [paper]

  • A Topology-aware Graph Coarsening Framework for Continual Graph Learning (NeurIPS 2024) [paper][code]

  • What Matters in Graph Class Incremental Learning? An Information Preservation Perspective (NeurIPS 2024) [paper][code]

  • Happy: A Debiased Learning Framework for Continual Generalized Category Discovery (NeurIPS 2024) [paper][code]

  • MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment (ECCV 2024) [paper][code]

  • Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation (ECCV24)[paper][code]

  • Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion (ECCV24)[paper][code]

  • Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection (ECCV24)[paper][code]

  • Confidence Self-Calibration for Multi-Label Class-Incremental Learning (ECCV24)[paper]

  • Rethinking Few-shot Class-incremental Learning: Learning from Yourself (ECCV24)[paper][code]

  • Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning (ECCV24)[paper][code]

  • Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer (ECCV24)[paper][code]

  • Mitigating Background Shift in Class-Incremental Semantic Segmentation (ECCV24)[paper][code]

  • Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching (ECCV24)[paper]

  • Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation (ECCV24)[paper][code]

  • Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental Semantic Segmentation (ECCV24)[paper]

  • Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation (ECCV24)[paper]

  • DiffClass: Diffusion-Based Class Incremental Learning (ECCV24)[paper][code]

  • PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning (ECCV24)[paper][code]

  • Few-shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt (ECCV24)[paper][code]

  • iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning (ECCV24)[paper][code]

  • Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation (ECCV24)[paper][code]

  • Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference (ECCV24)[paper][code]

  • Semantic Residual Prompts for Continual Learning (ECCV24)[paper][code]

  • Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery (ECCV24)[paper][code]

  • CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning (ECCV24)[paper][code]

  • Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning (ECCV24)[paper]

  • Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models (ECCV24)[paper][code]

  • Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation (ECCV24)[paper][code]

  • Revisiting Supervision for Continual Representation Learning (ECCV24)[paper][code]

  • Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models (ECCV24)[paper][code]

  • PromptFusion: Decoupling Stability and Plasticity for Continual Learning (ECCV24)[paper][code]

  • One-stage Prompt-based Continual Learning (ECCV24)[paper]

  • Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection (ECCV24)[paper][code]

  • Exemplar-free Continual Representation Learning via Learnable Drift Compensation (ECCV24)[paper][code]

  • Open-World Dynamic Prompt and Continual Visual Representation Learning (ECCV24)[paper]

  • Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning (ECCV24)[paper][code]

  • PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery (ECCV24)[paper][code]

  • MagMax: Leveraging Model Merging for Seamless Continual Learning (ECCV24)[paper][code]

  • Anytime Continual Learning for Open Vocabulary Classification (ECCV24)[paper][code]

  • Weighted Ensemble Models Are Strong Continual Learners (ECCV24)[paper][code]

  • CLEO: Continual Learning of Evolving Ontologies (ECCV24)[paper][code]

  • UNIKD: UNcertainty-Filtered Incremental Knowledge Distillation for Neural Implicit Representation (ECCV24)[paper][code]

  • Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning (ECCV24)[paper][code]

  • STSP: Spatial-Temporal Subspace Projection for Video Class-incremental Learning (ECCV24)[paper]

  • Non-Exemplar Domain Incremental Learning via Cross-Domain Concept Integration (ECCV24)[paper]

  • CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning (ECCV24)[paper][code]

  • On the Approximation Risk of Few-Shot Class-Incremental Learning (ECCV24)[paper][code]

  • Adapt without Forgetting: Distill Proximity from Dual Teachers in Vision-Language Models (ECCV24)[paper][code]

  • Information Bottleneck Based Data Correction in Continual Learning (ECCV24)[paper]

  • Continual Learning and Unknown Object Discovery in 3D Scenes via Self-Distillation (ECCV24)[paper][code]

  • Pick-a-back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning (ECCV24)[paper][code]

  • Human Motion Forecasting in Dynamic Domain Shifts: A Homeostatic Continual Test-time Adaptation Framework (ECCV24)[paper]

  • CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection (ECCV24)[paper][code]

  • RCS-Prompt: Learning Prompt to Rearrange Class Space for Prompt-based Continual Learning (ECCV24)[paper][code]

  • Online Continuous Generalized Category Discovery (ECCV24)[paper][code]

  • Class-incremental Learning for Time Series: Benchmark and Evaluation (KDD24)[paper][code]

  • Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning (ICML24)[paper]

  • Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning (ICML24)[paper][code]

  • Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning (ICML24)[paper]

  • Learning to Continually Learn with the Bayesian Principle (ICML24)[paper][code]

  • Rethinking Momentum Knowledge Distillation in Online Continual Learning (ICML24)[paper][code]

  • Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning (ICML24)[paper]

  • Bayesian Adaptation of Network Depth and Width for Continual Learning (ICML24)[paper]

  • STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment (ICML24)[paper][code]

  • On the Diminishing Returns of Width for Continual Learning (ICML24)[paper][code]

  • Compositional Few-Shot Class-Incremental Learning (ICML24)[paper][code]

  • Rapid Learning without Catastrophic Forgetting in the Morris Water Maze (ICML24)[paper][code]

  • Understanding Forgetting in Continual Learning with Linear Regression (ICML24)[paper]

  • Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization (ICML24)[paper]

  • Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning (ICML24)[paper]

  • Provable Contrastive Continual Learning (ICML24)[paper]

  • Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method (ICML24)[paper][code]

  • An Effective Dynamic Gradient Calibration Method for Continual Learning (ICML24)[paper]

  • Federated Continual Learning via Prompt-based Dual Knowledge Transfer (ICML24)[paper][code]

  • COPAL: Continual Pruning in Large Language Generative Models (ICML24)[paper]

  • One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning (ICML24)[paper]

  • Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition (TPAMI2024)[paper]

    • DELTA: Decoupling Long-Tailed Online Continual Learning (CVPR Workshop 2024)[paper][Code]
  • AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving (CVPR2024)[paper]

  • Continual Segmentation with Disentangled Objectness Learning and Class Recognition (CVPR2024)[paper][code]

  • Interactive Continual Learning: Fast and Slow Thinking (CVPR2024)[paper][code]

  • InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning (CVPR2024)[paper][code]

  • Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer (CVPR2024)[paper][code]

  • Traceable Federated Continual Learning (CVPR2024)[paper][code]

  • Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay (CVPR2024)[paper]

  • Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification (CVPR2024)[paper][code]

  • Towards Backward-Compatible Continual Learning of Image Compression (CVPR2024)[paper][code]

  • Class Incremental Learning with Multi-Teacher Distillation (CVPR2024)[paper][code]

  • Towards Efficient Replay in Federated Incremental Learning (CVPR2024)[paper]

  • Dual-consistency Model Inversion for Non-exemplar Class Incremental Learning (CVPR2024)[paper]

  • Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning (CVPR2024)[paper]

  • Coherent Temporal Synthesis for Incremental Action Segmentation (CVPR2024)[paper]

  • Text-Enhanced Data-free Approach for Federated Class-Incremental Learning (CVPR2024)[paper][code]

  • NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning (CVPR2024)[paper][code]

  • Long-Tail Class Incremental Learning via Independent Sub-prototype Construction (CVPR2024)[paper]

  • FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning (CVPR2024)[paper][code]

  • Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (CVPR2024)[paper][code]

  • Gradient Reweighting: Towards Imbalanced Class-Incremental Learning (CVPR2024)[paper][code]

  • OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning (CVPR2024)[paper][code]

  • SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection (CVPR2024)[paper]

  • Generative Multi-modal Models are Good Class Incremental Learners (CVPR2024)[paper][code]

  • Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning (CVPR2024)[paper][code]

  • DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning (CVPR2024)[paper][code]

  • Enhancing Visual Continual Learning with Language-Guided Supervision (CVPR2024)[paper]

  • Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters (CVPR2024)[paper][code]

  • Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (CVPR2024)[paper]

  • Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning (CVPR2024)[paper][code]

  • ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning (CVPR2024)[paper][code]

  • Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory (CVPR2024)[paper][code]

  • Learning from One Continuous Video Stream (CVPR2024)[paper]

  • Improving Plasticity in Online Continual Learning via Collaborative Learning (CVPR2024)[paper][code]

  • Learning Equi-angular Representations for Online Continual Learning (CVPR2024)[paper][code]

  • BrainWash: A Poisoning Attack to Forget in Continual Learning (CVPR2024)[paper]

  • Consistent Prompting for Rehearsal-Free Continual Learning (CVPR2024)[paper][code]

  • Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning (CVPR2024)[paper][code]

  • Convolutional Prompting meets Language Models for Continual Learning (CVPR2024)[paper][code]

  • Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (CVPR2024)[paper][code]

  • Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners (CVPR2024)[paper][code]

  • Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation (CVPR2024)[paper][code]

  • Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning (ICLR2024)[paper][code]

  • Function-space Parameterization of Neural Networks for Sequential Learning (ICLR2024)[paper]

  • Progressive Fourier Neural Representation for Sequential Video Compilation (ICLR2024)[paper]

  • Kalman Filter Online Classification from non-Stationary Data (ICLR2024)[paper]

  • Continual Momentum Filtering on Parameter Space for Online Test-time Adaptation (ICLR2024)[paper]

  • TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models (ICLR2024)[paper]

  • Class Incremental Learning via Likelihood Ratio Based Task Prediction (ICLR2024)[paper][code]

  • The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting - An Analytical Model (ICLR2024)[paper]

  • Prediction Error-based Classification for Class-Incremental Learning (ICLR2024)[paper][code]

  • Adapting Large Language Models via Reading Comprehension (ICLR2024)[paper][code]

  • Accurate Forgetting for Heterogeneous Federated Continual Learning (ICLR2024)[paper]

  • Fixed Non-negative Orthogonal Classifier: Inducing Zero-mean Neural Collapse with Feature Dimension Separation (ICLR2024)[paper]

  • A Probabilistic Framework for Modular Continual Learning (ICLR2024)[paper]

  • A Unified and General Framework for Continual Learning (ICLR2024)[paper]

  • Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation (ICLR2024)[paper]

  • CPPO: Continual Learning for Reinforcement Learning with Human Feedback (ICLR2024)[paper]

  • Online Continual Learning for Interactive Instruction Following Agents (ICLR2024)[paper][code]

  • Scalable Language Model with Generalized Continual Learning (ICLR2024)[paper]

  • ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation (ICLR2024)[paper]

  • Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks (ICLR2024)[paper][code]

  • TiC-CLIP: Continual Training of CLIP Models (ICLR2024)[paper]

  • Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline (ICLR2024)[paper]

  • Addressing Catastrophic Forgetting and Loss of Plasticity in Neural Networks (ICLR2024)[paper]

  • Locality Sensitive Sparse Encoding for Learning World Models Online (ICLR2024)[paper]

  • Dissecting learning and forgetting in language model finetuning (ICLR2024)[paper]

  • Prompt Gradient Projection for Continual Learning (ICLR2024)[paper][code]

  • Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time (ICLR2024)[paper]

  • Divide and not forget: Ensemble of selectively trained experts in Continual Learning (ICLR2024)[paper][code]

  • eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation (AAAI2024) [paper][code]

  • Evolving Parameterized Prompt Memory for Continual Learning (AAAI2024)[paper][code]

  • Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding (AAAI2024)[paper]

  • Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning (AAAI2024)[paper]

  • Class-Incremental Learning: Cross-Class Feature Augmentation for Class Incremental Learning (AAAI2024)[paper]

  • Learning Task-Aware Language-Image Representation for Class-Incremental Object Detection (AAAI2024)[paper]

  • MIND: Multi-Task Incremental Network Distillation (AAAI2024)[paper][code]

  • Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning (WACV2024)[paper][code]

  • Plasticity-Optimized Complementary Networks for Unsupervised Continual (WACV2024)[paper]

  • Online Class-Incremental Learning For Real-World Food Image Classification (WACV2024)[paper]

2023

  • RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
    (CoLLAs 2023) [paper] [code]
  • SIESTA: Efficient Online Continual Learning with Sleep (TMLR 2023)[paper]
  • Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (EMNLP 2023)[paper]
  • Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (Nature Machine Intelligence 2023) [paper]
  • Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork (NeurIPS 2023) [paper] [Code]
  • Loss Decoupling for Task-Agnostic Continual Learning (NeurIPS 2023) [paper]
  • Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm (NeurIPS 2023)[paper]
  • Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments (NeurIPS 2023)[paper]
  • An Efficient Dataset Condensation Plugin and Its Application to Continual Learning (NeurIPS 2023)[paper]
  • Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation (NeurIPS 2023)[paper]
  • Prediction and Control in Continual Reinforcement Learning (NeurIPS 2023)[paper]
  • On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm (NeurIPS 2023)[paper]
  • Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation (NeurIPS 2023)[paper]
  • A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks (NeurIPS 2023)[paper]
  • Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration (NeurIPS 2023)[paper]
  • A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm (NeurIPS 2023)[paper][code]
  • Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees (NeurIPS 2023)[paper][code]
  • Recasting Continual Learning as Sequence Modeling (NeurIPS 2023)[paper]
  • Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection (NeurIPS 2023)[paper]
  • Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework (NeurIPS 2023)[paper]
  • CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation (NeurIPS 2023)[paper]
  • TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion (NeurIPS 2023)[paper]
  • Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models (NeurIPS 2023)[paper]
  • A Definition of Continual Reinforcement Learning (NeurIPS 2023)[paper]
  • RanPAC: Random Projections and Pre-trained Models for Continual Learning (NeurIPS 2023)[paper]
  • Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality (NeurIPS 2023)[paper]
  • FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning (NeurIPS 2023)[paper]
  • The Ideal Continual Learner: An Agent That Never Forgets (ICML2023) [paper]
  • Continual Learners are Incremental Model Generalizers (ICML2023)[paper]
  • Learnability and Algorithm for Continual Learning (ICML2023)[paper][code]
  • Parameter-Level Soft-Masking for Continual Learning (ICML2023)[paper]
  • Continual Learning in Linear Classification on Separable Data (ICML2023)[paper]
  • DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning (ICML2023)[paper]
  • BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning (ICML2023)[paper]
  • DDGR: Continual Learning with Deep Diffusion-based Generative Replay (ICML2023)[paper]
  • Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal (ICML2023)[paper]
  • Theory on Forgetting and Generalization of Continual Learning (ICML2023)[paper]
  • Poisoning Generative Replay in Continual Learning to Promote Forgetting (ICML2023)[paper]
  • Continual Vision-Language Representation Learning with Off-Diagonal Information (ICML2023)[paper]
  • Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning (ICML2023)[paper]
  • Does Continual Learning Equally Forget All Parameters? (ICML2023)[paper]
  • Growing a Brain with Sparsity-Inducing Generation for Continual Learning (ICCV 2023)[paper)][code]
  • Self-regulating Prompts: Foundational Model Adaptation without Forgetting (ICCV 2023)[paper][code]
  • Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning (ICCV 2023)[paper][code]
  • Tangent Model Composition for Ensembling and Continual Fine-tuning (ICCV 2023)[paper][code]
  • CBA: Improving Online Continual Learning via Continual Bias Adaptor (ICCV 2023)[paper]
  • CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation (ICCV 2023)[paper][code]
  • NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning (ICCV 2023)[paper][code]
  • Online Continual Learning on Hierarchical Label Expansion (ICCV 2023)[paper]
  • Class-Incremental Grouping Network for Continual Audio-Visual Learning (ICCV 2023)[paper][code]
  • Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? (ICCV 2023)[paper][code]
  • When Prompt-based Incremental Learning Does Not Meet Strong Pretraining (ICCV 2023)[paper]
  • Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning (ICCV 2023)[paper][code]
  • Dynamic Residual Classifier for Class Incremental Learning (ICCV 2023)[paper]
  • First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning (ICCV 2023)[paper]
  • Masked Autoencoders are Efficient Class Incremental Learners (ICCV 2023)[paper]
  • Introducing Language Guidance in Prompt-based Continual Learning (ICCV 2023)[paper]
  • CLNeRF: Continual Learning Meets NeRFs (ICCV 2023)[paper]
  • Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models (ICCV 2023)[paper][code]
  • LFS-GAN: Lifelong Few-Shot Image Generation (ICCV 2023)[paper]
  • TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation (ICCV 2023)[paper]
  • Learning to Learn: How to Continuously Teach Humans and Machines (ICCV 2023)[paper]
  • Audio-Visual Class-Incremental Learning (ICCV 2023)[paper][code]
  • MetaGCD: Learning to Continually Learn in Generalized Category Discovery (ICCV 2023)[paper]
  • Exemplar-Free Continual Transformer with Convolutions (ICCV 2023)[paper][code]
  • A Unified Continual Learning Framework with General Parameter-Efficient Tuning (ICCV 2023)[paper]
  • Incremental Generalized Category Discovery (ICCV 2023)[paper]
  • Heterogeneous Forgetting Compensation for Class-Incremental Learning (ICCV 2023)[paper][code]
  • Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection (ICCV 2023)[paper][code]
  • MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition (ICCV 2023)[paper][code]
  • CLR: Channel-wise Lightweight Reprogramming for Continual Learning (ICCV 2023)[paper][code]
  • ICICLE: Interpretable Class Incremental Continual Learning (ICCV 2023)[paper]
  • Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery (ICCV 2023)[paper]
  • SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model (ICCV 2023)[paper][code]
  • Online Prototype Learning for Online Continual Learning (ICCV 2023)[paper][code]
  • Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (ACL2023)[paper]
  • Class-Incremental Learning based on Label Generation (ACL2023)[paper]
  • Computationally Budgeted Continual Learning: What Does Matter? (CVPR2023)[paper][code]
  • Real-Time Evaluation in Online Continual Learning: A New Hope (CVPR2023)[paper]
  • Dealing With Cross-Task Class Discrimination in Online Continual Learning (CVPR2023)[paper][code]
  • Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning (CVPR2023)[paper][code]
  • GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-shot Class Incremental Task (CVPR2023)[paper]
  • EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization (CVPR2023)[paper]
  • Endpoints Weight Fusion for Class Incremental Semantic Segmentation (CVPR2023)[paper]
  • On the Stability-Plasticity Dilemma of Class-Incremental Learning (CVPR2023)[paper]
  • Regularizing Second-Order Influences for Continual Learning (CVPR2023)[paper][code]
  • Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning (CVPR2023)[paper]
  • Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning (CVPR2023)[paper]
  • A Probabilistic Framework for Lifelong Test-Time Adaptation (CVPR2023)[paper][code]
  • Continual Semantic Segmentation with Automatic Memory Sample Selection (CVPR2023)[paper]
  • Exploring Data Geometry for Continual Learning (CVPR2023)[paper]
  • PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning (CVPR2023)[paper][code]
  • Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning (CVPR2023)[paper][code]
  • Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation (CVPR2023)[paper]
  • Continual Detection Transformer for Incremental Object Detection (CVPR2023)[paper][code]
  • PIVOT: Prompting for Video Continual Learning (CVPR2023)[paper]
  • CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning (CVPR2023)[paper][code]
  • Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions (CVPR2023)[paper]
  • Class-Incremental Exemplar Compression for Class-Incremental Learning (CVPR2023)[paper][code]
  • Dense Network Expansion for Class Incremental Learning (CVPR2023)[paper]
  • Online Bias Correction for Task-Free Continual Learning (ICLR2023)[paper]
  • Sparse Distributed Memory is a Continual Learner (ICLR2023)[paper]
  • Continual Learning of Language Models (ICLR2023)[paper]
  • Progressive Prompts: Continual Learning for Language Models without Forgetting (ICLR2023)[paper]
  • Is Forgetting Less a Good Inductive Bias for Forward Transfer? (ICLR2023)[paper]
  • Online Boundary-Free Continual Learning by Scheduled Data Prior (ICLR2023)[paper]
  • Incremental Learning of Structured Memory via Closed-Loop Transcription (ICLR2023)[paper]
  • Better Generative Replay for Continual Federated Learning (ICLR2023)[paper]
  • 3EF: Class-Incremental Learning via Efficient Energy-Based Expansion and Fusion (ICLR2023)[paper]
  • Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning (ICLR2023)[paper]
  • Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting (ICLR2023)[paper]
  • Building a Subspace of Policies for Scalable Continual Learning (ICLR2023)[paper]
  • A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning (ICLR2023)[paper]
  • Continual evaluation for lifelong learning: Identifying the stability gap (ICLR2023)[paper]
  • Continual Unsupervised Disentangling of Self-Organizing Representations (ICLR2023)[paper]
  • Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning (ICLR2023)[paper]
  • Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning (ICLR2023)[paper]
  • On the Soft-Subnetwork for Few-Shot Class Incremental Learning (ICLR2023)[paper]
  • Task-Aware Information Routing from Common Representation Space in Lifelong Learning (ICLR2023)[paper]
  • Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning (ICLR2023)[paper]
  • Neural Weight Search for Scalable Task Incremental Learning (WACV2023)[paper]
  • Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation (WACV2023)[paper]
  • FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning (WACV2023)[paper]
  • Do Pre-trained Models Benefit Equally in Continual Learning? (WACV2023)[paper] [code]
  • Sparse Coding in a Dual Memory System for Lifelong Learning (AAAI2023)[paper] [code]

2022

  • Online Continual Learning through Mutual Information Maximization (ICML2022)[paper]

  • Prototype-guided continual adaptation for class-incremental unsupervised domain adaptation (ECCV2022)[paper] [code]

  • Balanced softmax cross-entropy for incremental learning with and without memory (CVIU)[paper]

  • Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (COLING2022) [paper] [code]

  • Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (ICML2022)[paper]

  • Forget-free Continual Learning with Winning Subnetworks (ICML2022)[paper]

  • NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (ICML2022)[paper]

  • Continual Learning via Sequential Function-Space Variational Inference (ICML2022)[paper]

  • A Theoretical Study on Solving Continual Learning (NeurIPS2022) [paper] [code]

  • ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection (NeurIPS2022) [paper]

  • Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer (NeurIPS2022) [paper]

  • Memory Efficient Continual Learning with Transformers (NeurIPS2022) [paper]

  • Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation (NeurIPS2022) [paper] [code]

  • Disentangling Transfer in Continual Reinforcement Learning (NeurIPS2022) [paper]

  • Task-Free Continual Learning via Online Discrepancy Distance Learning (NeurIPS2022) [paper]

  • A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal (NeurIPS2022) [paper]

  • S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning (NeurIPS2022) [paper]

  • Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting (NeurIPS2022) [paper]

  • Few-Shot Continual Active Learning by a Robot (NeurIPS2022) [paper]

  • Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions(NeurIPS2022) [paper]

  • SparCL: Sparse Continual Learning on the Edge(NeurIPS2022) [paper]

  • CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks (NeurIPS2022) [paper] [code]

  • Continual Learning In Environments With Polynomial Mixing Times (NeurIPS2022) [paper] [code]

  • Exploring Example Influence in Continual Learning (NeurIPS2022) [paper] [code]

  • ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation (NeurIPS2022) [paper]

  • On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (NeurIPS2022) [paper] [code]

  • On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting (NeurIPS2022)[paper]

  • CGLB: Benchmark Tasks for Continual Graph Learning (NeurIPS2022)[paper] [code]

  • How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? (NeurIPS2022)[paper]

  • CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One (ECCV2022)[paper] [code]

  • Generative Negative Text Replay for Continual Vision-Language Pretraining (ECCV2022) [paper]

  • DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning (ECCV2022) [paper] [code]

  • The Challenges of Continuous Self-Supervised Learning (ECCV2022)[paper]

  • Helpful or Harmful: Inter-Task Association in Continual Learning (ECCV2022)[paper]

  • incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection (ECCV2022)[paper]

  • S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning (ECCV2022)[paper]

  • Online Task-free Continual Learning with Dynamic Sparse Distributed Memory (ECCV2022)[paper][code]

  • Balancing between Forgetting and Acquisition in Incremental Subpopulation Learning (ECCV2022)[paper]

  • Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer (ECCV2022) [paper] [code]

  • FOSTER: Feature Boosting and Compression for Class-Incremental Learning (ECCV2022) [paper] [code]

  • Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (ECCV2022) [paper]

  • R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning (ECCV2022) [paper] [code]

  • DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning (ECCV2022) [paper]

  • Learning with Recoverable Forgetting (ECCV2022) [paper]

  • Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (ECCV2022) [paper] [code]

  • Balancing Stability and Plasticity through Advanced Null Space in Continual Learning (ECCV2022) [paper]

  • Long-Tailed Class Incremental Learning (ECCV2022) [paper]

  • Anti-Retroactive Interference for Lifelong Learning (ECCV2022) [paper]

  • Novel Class Discovery without Forgetting (ECCV2022) [paper]

  • Class-incremental Novel Class Discovery (ECCV2022) [paper]

  • Few-Shot Class Incremental Learning From an Open-Set Perspective(ECCV2022)[paper]

  • Incremental Task Learning with Incremental Rank Updates(ECCV2022)[paper]

  • Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay(ECCV2022)[paper]

  • Online Continual Learning with Contrastive Vision Transformer (ECCV2022)[paper]

  • Transfer without Forgetting (ECCV2022) [paper][code]

  • Continual Training of Language Models for Few-Shot Learning (EMNLP2022) [paper] [code]

  • Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (TPAMI2022) [paper]

  • MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning (TPAMI2022) [paper]

  • Class-Incremental Continual Learning into the eXtended DER-verse (TPAMI2022) [paper] [code]

  • Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks (TPAMI2022) [paper] [code]

  • Continual Semi-Supervised Learning through Contrastive Interpolation Consistency (PRL2022) [paper][code]

  • GCR: Gradient Coreset Based Replay Buffer Selection for Continual Learning (CVPR2022) [paper]

  • Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning (CVPR2022) [paper]

  • Continual Learning With Lifelong Vision Transformer (CVPR2022) [paper]

  • Towards Better Plasticity-Stability Trade-Off in Incremental Learning: A Simple Linear Connector (CVPR2022) [paper]

  • Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (CVPR2022) [paper]

  • Continual Learning for Visual Search with Backward Consistent Feature Embedding (CVPR2022) [paper]

  • Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (CVPR2022) [paper]

  • Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency (CVPR2022) [paper]

  • Bring Evanescent Representations to Life in Lifelong Class Incremental Learning (CVPR2022) [paper]

  • Lifelong Graph Learning (CVPR2022) [paper]

  • Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation (CVPR2022) [paper]

  • vCLIMB: A Novel Video Class Incremental Learning Benchmark (CVPR2022) [paper]

  • Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation(CVPR2022) [paper]

  • Few-Shot Incremental Learning for Label-to-Image Translation (CVPR2022) [paper]

  • MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning (CVPR2022) [paper]

  • Incremental Learning in Semantic Segmentation from Image Labels (CVPR2022) [paper]

  • Self-Supervised Models are Continual Learners (CVPR2022) [paper] [code]

  • Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data (CVPR2022) [paper]

  • General Incremental Learning with Domain-aware Categorical Representations (CVPR2022) [paper]

  • Constrained Few-shot Class-incremental Learning (CVPR2022) [paper]

  • Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation (CVPR2022) [paper]

  • Class-Incremental Learning with Strong Pre-trained Models (CVPR2022) [paper]

  • Energy-based Latent Aligner for Incremental Learning (CVPR2022) [paper] [code]

  • Meta-attention for ViT-backed Continual Learning (CVPR2022) [paper] [code]

  • Learning to Prompt for Continual Learning (CVPR2022) [paper] [code]

  • On Generalizing Beyond Domains in Cross-Domain Continual Learning (CVPR2022) [paper]

  • Probing Representation Forgetting in Supervised and Unsupervised Continual Learning (CVPR2022) [paper]

  • Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding (CVPR2022) [paper] [code]

  • Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning (CVPR2022) [paper] [code]

  • Forward Compatible Few-Shot Class-Incremental Learning (CVPR2022) [paper] [code]

  • Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (CVPR2022) [paper]

  • DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion (CVPR2022) [paper]

  • Federated Class-Incremental Learning (CVPR2022) [paper] [code]

  • Representation Compensation Networks for Continual Semantic Segmentation (CVPR2022) [paper]

  • A Multi-Head Model for Continual Learning via Out-of-Distribution Replay (CoLLAs2022) [paper] [code]

  • Continual Attentive Fusion for Incremental Learning in Semantic Segmentation (TMM2022) [paper]

  • Self-training for class-incremental semantic segmentation (TNNLS2022) [paper]

  • Effects of Auxiliary Knowledge on Continual Learning (ICPR2022) [paper]

  • Continual Sequence Generation with Adaptive Compositional Modules (ACL2022) [paper]

  • Learngene: From Open-World to Your Learning Task (AAAI2022) [paper] [code]

  • Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (ICLR2022) [paper]

  • Continual Learning with Filter Atom Swapping (ICLR2022) [paper]

  • Continual Learning with Recursive Gradient Optimization (ICLR2022) [paper]

  • TRGP: Trust Region Gradient Projection for Continual Learning (ICLR2022) [paper]

  • Looking Back on Learned Experiences For Class/task Incremental Learning (ICLR2022) [paper]

  • Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR2022) [paper]

  • Model Zoo: A Growing Brain That Learns Continually (ICLR2022) [paper]

  • Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (ICLR2022) [paper]

  • Memory Replay with Data Compression for Continual Learning (ICLR2022) [paper]

  • Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System (ICLR2022) [paper]

  • Online Coreset Selection for Rehearsal-based Continual Learning (ICLR2022) [paper]

  • Pretrained Language Model in Continual Learning: A Comparative Study (ICLR2022) [paper]

  • Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR2022) [paper]

  • New Insights on Reducing Abrupt Representation Change in Online Continual Learning (ICLR2022) [paper]

  • Towards Continual Knowledge Learning of Language Models (ICLR2022) [paper]

  • CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (ICLR2022) [paper]

  • CoMPS: Continual Meta Policy Search (ICLR2022) [paper]

  • Information-theoretic Online Memory Selection for Continual Learning (ICLR2022) [paper]

  • Subspace Regularizers for Few-Shot Class Incremental Learning (ICLR2022) [paper]

  • LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (ICLR2022) [paper]

  • Effect of scale on catastrophic forgetting in neural networks (ICLR2022) [paper]

  • Dataset Knowledge Transfer for Class-Incremental Learning without Memory (WACV2022) [paper]

  • Knowledge Capture and Replay for Continual Learning (WACV2022) [paper]

  • Online Continual Learning via Candidates Voting (WACV2022) [paper]

  • lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents (IJCNN2022) [paper]

  • Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (Journal of Imaging 2022) [paper]

2021

  • Incremental Object Detection via Meta-Learning (TPAMI 2021) [paper] [code]
  • Triple-Memory Networks: A Brain-Inspired Method for Continual Learning (TNNLS 2021) [paper]
  • Memory efficient class-incremental learning for image classification (TNNLS 2021) [paper]
  • A Procedural World Generation Framework for Systematic Evaluation of Continual Learning (NeurIPS2021) [paper]
  • Class-Incremental Learning via Dual Augmentation (NeurIPS2021) [paper]
  • SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (NeurIPS2021) [paper]
  • RMM: Reinforced Memory Management for Class-Incremental Learning (NeurIPS2021) [paper]
  • Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima (NeurIPS2021) [paper]
  • Lifelong Domain Adaptation via Consolidated Internal Distribution (NeurIPS2021) [paper]
  • AFEC: Active Forgetting of Negative Transfer in Continual Learning (NeurIPS2021) [paper]
  • Natural continual learning: success is a journey, not (just) a destination (NeurIPS2021) [paper]
  • Gradient-based Editing of Memory Examples for Online Task-free Continual Learning (NeurIPS2021) [paper]
  • Optimizing Reusable Knowledge for Continual Learning via Metalearning (NeurIPS2021) [paper]
  • Formalizing the Generalization-Forgetting Trade-off in Continual Learning (NeurIPS2021) [paper]
  • Learning where to learn: Gradient sparsity in meta and continual learning (NeurIPS2021) [paper]
  • Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (NeurIPS2021) [paper]
  • Posterior Meta-Replay for Continual Learning (NeurIPS2021) [paper]
  • Continual Auxiliary Task Learning (NeurIPS2021) [paper]
  • Mitigating Forgetting in Online Continual Learning with Neuron Calibration (NeurIPS2021) [paper]
  • BNS: Building Network Structures Dynamically for Continual Learning (NeurIPS2021) [paper]
  • DualNet: Continual Learning, Fast and Slow (NeurIPS2021) [paper]
  • BooVAE: Boosting Approach for Continual Learning of VAE (NeurIPS2021) [paper]
  • Generative vs. Discriminative: Rethinking The Meta-Continual Learning (NeurIPS2021) [paper]
  • Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (NeurIPS2021) [paper]
  • Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (NeurIPS, 2021) [paper] [code]
  • SS-IL: Separated Softmax for Incremental Learning (ICCV, 2021) [paper]
  • Striking a Balance between Stability and Plasticity for Class-Incremental Learning (ICCV, 2021) [paper]
  • Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (ICCV, 2021) [paper]
  • Class-Incremental Learning for Action Recognition in Videos (ICCV, 2021) [paper]
  • Continual Prototype Evolution:Learning Online from Non-Stationary Data Streams (ICCV, 2021) [paper]
  • Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning (ICCV, 2021) [paper]
  • Co2L: Contrastive Continual Learning (ICCV, 2021) [paper]
  • Wanderlust: Online Continual Object Detection in the Real World (ICCV, 2021) [paper]
  • Continual Learning on Noisy Data Streams via Self-Purified Replay (ICCV, 2021) [paper]
  • Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data (ICCV, 2021) [paper]
  • Detection and Continual Learning of Novel Face Presentation Attacks (ICCV, 2021) [paper]
  • Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data (ICCV, 2021) [paper]
  • Continual Learning for Image-Based Camera Localization (ICCV, 2021) [paper]
  • Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting (ICCV, 2021) [paper]
  • Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (ICCV, 2021) [paper]
  • RECALL: Replay-based Continual Learning in Semantic Segmentation (ICCV, 2021) [paper]
  • Few-Shot and Continual Learning with Attentive Independent Mechanisms (ICCV, 2021) [paper]
  • Learning with Selective Forgetting (IJCAI, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Kernel Continual Learning (ICML, 2021) [paper]
  • Variational Auto-Regressive Gaussian Processes for Continual Learning (ICML, 2021) [paper]
  • Bayesian Structural Adaptation for Continual Learning (ICML, 2021) [paper]
  • Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (ICML, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Federated Continual Learning with Weighted Inter-client Transfer (ICML, 2021) [paper]
  • Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (NAACL, 2021) [paper]
  • Continual Learning for Text Classification with Information Disentanglement Based Regularization (NAACL, 2021) [paper]
  • CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (EMNLP, 2021) [paper][code]
  • Co-Transport for Class-Incremental Learning (ACM MM, 2021) [paper]
  • Towards Open World Object Detection (CVPR, 2021) [paper] [code] [video]
  • Prototype Augmentation and Self-Supervision for Incremental Learning (CVPR, 2021) [paper] [code]
  • ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (CVPR, 2021) [paper]
  • Incremental Learning via Rate Reduction (CVPR, 2021) [paper]
  • IIRC: Incremental Implicitly-Refined Classification (CVPR, 2021) [paper]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning (CVPR, 2021) [paper]
  • Image De-raining via Continual Learning (CVPR, 2021) [paper]
  • Continual Learning via Bit-Level Information Preserving (CVPR, 2021) [paper]
  • Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation (CVPR, 2021) [paper]
  • Lifelong Person Re-Identification via Adaptive Knowledge Accumulation (CVPR, 2021) [paper]
  • Distilling Causal Effect of Data in Class-Incremental Learning (CVPR, 2021) [paper]
  • Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
  • Layerwise Optimization by Gradient Decomposition for Continual Learning (CVPR, 2021) [paper]
  • Adaptive Aggregation Networks for Class-Incremental Learning (CVPR, 2021) [paper]
  • Incremental Few-Shot Instance Segmentation (CVPR, 2021) [paper]
  • Efficient Feature Transformations for Discriminative and Generative Continual Learning (CVPR, 2021) [paper]
  • On Learning the Geodesic Path for Incremental Learning (CVPR, 2021) [paper]
  • Few-Shot Incremental Learning with Continually Evolved Classifiers (CVPR, 2021) [paper]
  • Rectification-based Knowledge Retention for Continual Learning (CVPR, 2021) [paper]
  • DER: Dynamically Expandable Representation for Class Incremental Learning (CVPR, 2021) [paper]
  • Rainbow Memory: Continual Learning with a Memory of Diverse Samples (CVPR, 2021) [paper]
  • Training Networks in Null Space of Feature Covariance for Continual Learning
    (CVPR, 2021) [paper]
  • Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
    (CVPR, 2021) [paper]
  • PLOP: Learning without Forgetting for Continual Semantic Segmentation
    (CVPR, 2021) [paper]
  • Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
    (CVPR, 2021) [paper]
  • Online Class-Incremental Continual Learning with Adversarial Shapley Value(AAAI, 2021) [paper] [code]
  • Lifelong and Continual Learning Dialogue Systems: Learning during Conversation(AAAI, 2021) [paper]
  • Continual learning for named entity recognition(AAAI, 2021) [paper]
  • Using Hindsight to Anchor Past Knowledge in Continual Learning(AAAI, 2021) [paper]
  • Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network(AAAI, 2021) [paper] [code]
  • Curriculum-Meta Learning for Order-Robust Continual Relation Extraction(AAAI, 2021) [paper]
  • Continual Learning by Using Information of Each Class Holistically(AAAI, 2021) [paper]
  • Gradient Regularized Contrastive Learning for Continual Domain Adaptation(AAAI, 2021) [paper]
  • Unsupervised Model Adaptation for Continual Semantic Segmentation(AAAI, 2021) [paper]
  • A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation(AAAI, 2021) [paper]
  • Do Not Forget to Attend to Uncertainty While Mitigating Catastrophic Forgetting(WACV, 2021) [paper]
  • SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments (DAC2021) [paper]

2020

  • Rethinking Experience Replay: a Bag of Tricks for Continual Learning(ICPR, 2020) [paper] [code]
  • Continual Learning for Natural Language Generation in Task-oriented Dialog Systems(EMNLP, 2020) [paper]
  • Distill and Replay for Continual Language Learning(COLING, 2020) [paper]
  • Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks (NeurIPS2020) [paper] [code]
  • Meta-Consolidation for Continual Learning (NeurIPS2020) [paper]
  • Understanding the Role of Training Regimes in Continual Learning (NeurIPS2020) [paper]
  • Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (NeurIPS2020) [paper]
  • Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning (NeurIPS2020) [paper]
  • Coresets via Bilevel Optimization for Continual Learning and Streaming (NeurIPS2020) [paper]
  • RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning (NeurIPS2020) [paper]
  • Continual Deep Learning by Functional Regularisation of Memorable Past (NeurIPS2020) [paper]
  • Dark Experience for General Continual Learning: a Strong, Simple Baseline (NeurIPS2020) [paper] [code]
  • GAN Memory with No Forgetting (NeurIPS2020) [paper]
  • Calibrating CNNs for Lifelong Learning (NeurIPS2020) [paper]
  • Mitigating Forgetting in Online Continual Learning
    via Instance-Aware Parameterization (NeurIPS2020) [paper]
  • ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation(RecSys, 2020) [paper]
  • Initial Classifier Weights Replay for Memoryless Class Incremental Learning (BMVC2020) [paper]
  • Adversarial Continual Learning (ECCV2020) [paper] [code]
  • REMIND Your Neural Network to Prevent Catastrophic Forgetting (ECCV2020) [paper] [code]
  • Incremental Meta-Learning via Indirect Discriminant Alignment (ECCV2020) [paper]
  • Memory-Efficient Incremental Learning Through Feature Adaptation (ECCV2020) [paper]
  • PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (ECCV2020) [paper] [code]
  • Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference (ECCV2020) [paper]
  • Learning latent representions across multiple data domains using Lifelong VAEGAN (ECCV2020) [paper]
  • Online Continual Learning under Extreme Memory Constraints (ECCV2020) [paper]
  • Class-Incremental Domain Adaptation (ECCV2020) [paper]
  • More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (ECCV2020) [paper]
  • Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (ECCV2020) [paper]
  • GDumb: A Simple Approach that Questions Our Progress in Continual Learning (ECCV2020) [paper]
  • Imbalanced Continual Learning with Partitioning Reservoir Sampling (ECCV2020) [paper]
  • Topology-Preserving Class-Incremental Learning (ECCV2020) [paper]
  • GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (CIKM2020) [paper]
  • OvA-INN: Continual Learning with Invertible Neural Networks (IJCNN2020) [paper]
  • XtarNet: Learning to Extract Task-Adaptive Representation
    for Incremental Few-Shot Learning (ICLM2020) [paper]
  • Optimal Continual Learning has Perfect Memory and is NP-HARD (ICML2020) [paper]
  • Neural Topic Modeling with Continual Lifelong Learning (ICML2020) [paper]
  • Continual Learning with Knowledge Transfer for Sentiment Classification (ECML-PKDD2020) [paper] [code]
  • Semantic Drift Compensation for Class-Incremental Learning (CVPR2020) [paper] [code]
  • Few-Shot Class-Incremental Learning (CVPR2020) [paper]
  • Modeling the Background for Incremental Learning in Semantic Segmentation (CVPR2020) [paper]
  • Incremental Few-Shot Object Detection (CVPR2020) [paper]
  • Incremental Learning In Online Scenario (CVPR2020) [paper]
  • Maintaining Discrimination and Fairness in Class Incremental Learning (CVPR2020) [paper]
  • Conditional Channel Gated Networks for Task-Aware Continual Learning (CVPR2020) [paper]
  • Continual Learning with Extended Kronecker-factored Approximate Curvature
    (CVPR2020) [paper]
  • iTAML : An Incremental Task-Agnostic Meta-learning Approach (CVPR2020) [paper] [code]
  • Mnemonics Training: Multi-Class Incremental Learning without Forgetting (CVPR2020) [paper] [code]
  • ScaIL: Classifier Weights Scaling for Class Incremental Learning (WACV2020) [paper]
  • Accepted papers(ICLR2020) [paper]
  • Brain-inspired replay for continual learning with artificial neural networks (Natrue Communications 2020) [paper] [code]
  • Learning to Continually Learn (ECAI 2020) [paper] [code]

2019

  • Compacting, Picking and Growing for Unforgetting Continual Learning (NeurIPS2019)[paper][code]
  • Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning (ICMR2019) [paper][code]
  • Towards Training Recurrent Neural Networks for Lifelong Learning (Neural Computation 2019) [paper]
  • Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (IJCAI2019) [paper]
  • IL2M: Class Incremental Learning With Dual Memory
    (ICCV2019) [paper]
  • Incremental Learning Using Conditional Adversarial Networks
    (ICCV2019) [paper]
  • Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (KDD2019) [paper]
  • Random Path Selection for Incremental Learning (NeurIPS2019) [paper]
  • Online Continual Learning with Maximal Interfered Retrieval (NeurIPS2019) [paper]
  • Meta-Learning Representations for Continual Learning (NeurIPS2019) [paper] [code]
  • Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (ICCV2019) [paper]
  • Continual Learning by Asymmetric Loss Approximation
    with Single-Side Overestimation (ICCV2019) [paper]
  • Lifelong GAN: Continual Learning for Conditional Image Generation (ICCV2019) [paper]
  • Continual learning of context-dependent processing in neural networks (Nature Machine Intelligence 2019) [paper] [code]
  • Large Scale Incremental Learning (CVPR2019) [paper] [code]
  • Learning a Unified Classifier Incrementally via Rebalancing (CVPR2019) [paper] [code]
  • Learning Without Memorizing (CVPR2019) [paper]
  • Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (CVPR2019) [paper]
  • Task-Free Continual Learning (CVPR2019) [paper]
  • Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (ICML2019) [paper]
  • Efficient Lifelong Learning with A-GEM (ICLR2019) [paper] [code]
  • Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (ICLR2019) [paper] [code]
  • Overcoming Catastrophic Forgetting via Model Adaptation (ICLR2019) [paper]
  • A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR2019) [paper]

2018

  • Memory Replay GANs: learning to generate images from new categories without forgetting
    (NIPS2018) [paper] [code]
  • Reinforced Continual Learning (NIPS2018) [paper] [code]
  • Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting (NIPS2018) [paper]
  • Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (R-EWC) (ICPR2018) [paper] [code]
  • Exemplar-Supported Generative Reproduction for Class Incremental Learning (BMVC2018) [paper] [code]
  • End-to-End Incremental Learning (ECCV2018) [paper][code]
  • Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence (ECCV2018)[paper]
  • Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV2018) [paper] [code]
  • Memory Aware Synapses: Learning what (not) to forget (ECCV2018) [paper] [code]
  • Lifelong Learning via Progressive Distillation and Retrospection (ECCV2018) [paper]
  • PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR2018) [paper] [code]
  • Overcoming Catastrophic Forgetting with Hard Attention to the Task (ICML2018) [paper] [code]
  • Lifelong Learning with Dynamically Expandable Networks (ICLR2018) [paper]
  • FearNet: Brain-Inspired Model for Incremental Learning (ICLR2018) [paper]

2017

  • Incremental Learning of Object Detectors Without Catastrophic Forgetting
    (ICCV2017) [paper]
  • Overcoming catastrophic forgetting in neural networks (EWC) (PNAS2017) [paper] [code] [code]
  • Continual Learning Through Synaptic Intelligence (ICML2017) [paper] [code]
  • Gradient Episodic Memory for Continual Learning (NIPS2017) [paper] [code]
  • iCaRL: Incremental Classifier and Representation Learning (CVPR2017) [paper] [code]
  • Continual Learning with Deep Generative Replay (NIPS2017) [paper] [code]
  • Overcoming Catastrophic Forgetting by Incremental Moment Matching (NIPS2017) [paper] [code]
  • Expert Gate: Lifelong Learning with a Network of Experts (CVPR2017) [paper]
  • Encoder Based Lifelong Learning (ICCV2017) [paper]

2016

  • Learning without forgetting (ECCV2016) [paper] [code]

[Awesome Long-Tailed Recognition / Imbalanced Learning](https://github.com/xialeiliu/Awesome-LongTailed-Recognition)

Find it interesting that there are more shared techniques than I thought for incremental learning (exemplars-based).

ContinualAI wiki

[An Open Community of Researchers and Enthusiasts on Continual/Lifelong Learning for AI](https://www.continualai.org/)

Workshops

[4th Lifelong Learning Workshop at ICML 2020](https://lifelongml.github.io/)

[Workshop on Continual Learning at ICML 2020](https://icml.cc/Conferences/2020/Schedule?showEvent=5743)

[Continual Learning in Computer Vision Workshop CVPR 2020](https://sites.google.com/view/clvision2020/overview)

[Continual learning workshop NeurIPS 2018](https://sites.google.com/view/continual2018/home?authuser=0)

Challenges or Competitions

[1st Lifelong Learning for Machine Translation Shared Task at WMT20 (EMNLP 2020)](http://www.statmt.org/wmt20/lifelong-learning-task.html)

[Continual Learning in Computer Vision Challenge CVPR 2020](https://sites.google.com/view/clvision2020/challenge?authuser=0)

[Lifelong Robotic Vision Challenge IROS 2019](https://lifelong-robotic-vision.github.io)

Feel free to contact me if you find any interesting paper is missing.

Workshop papers are currently out due to space.


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