README
Awesome Self-Supervised Learning[](https://awesome.re)
A curated list of awesome Self-Supervised Learning resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search
#### Why Self-Supervised?
Self-Supervised Learning has become an exciting direction in AI community.
- Jitendra Malik: "Supervision is the opium of the AI researcher"
- Alyosha Efros: "The AI revolution will not be supervised"
- Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake"
Contributing
<p align="center">
<img src="http://cdn1.sportngin.com/attachments/news_article/7269/5172/needyou_small.jpg" alt="We Need You!">
</p>
Please help contribute this list by pull request
Markdown format:
- Paper Name.
[[pdf]](link)
[[code]](link)
- Author 1, Author 2, and Author 3. Conference YearTable of Contents
- Theory
- Computer Vision (CV)
- Survey
- Image Representation Learning
- Video Representation Learning
- 3D Feature Learning
- Geometry
- Audio
- Others
- Machine Learning
- Reinforcement Learning
- Recommendation Systems
- Robotics
- Natural Language Processing (NLP)
- Automatic Speech Recognition (ASR)
- Time-Series
- Graph
- Talks
- Thesis
- Blog
Theory
#### 2019
- A Theoretical Analysis of Contrastive Unsupervised Representation Learning.
[[pdf]](https://arxiv.org/pdf/1902.09229.pdf)
- Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi. ICML 2019
#### 2020
- Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.
[[pdf]](https://arxiv.org/pdf/2005.10242)
- Tongzhou Wang, Phillip Isola. ICML 2020
- Understanding Self-supervised Learning with Dual Deep Networks.
[[pdf]](https://arxiv.org/pdf/2010.00578.pdf)
- Yuandong Tian, Lantao Yu, Xinlei Chen, and Surya Ganguli.
- For self-supervised learning, Rationality implies generalization, provably.
[[pdf]](https://arxiv.org/pdf/2010.08508.pdf)
- Yamini Bansal, Gal Kaplun, and Boaz Barak.
#### 2021
- Towards the Generalization of Contrastive Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/2111.00743.pdf)
- Weiran Huang, Mingyang Yi, and Xuyang Zhao.
- Understanding the Behaviour of Contrastive Loss.
[[pdf]](https://arxiv.org/pdf/2012.09740.pdf)
- Feng Wang and Huaping Liu. CVPR 2021
- Predicting What You Already Know Helps: Provable Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/2008.01064.pdf)
- Jason D. Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo.
- Contrastive learning , multi-view redundancy , and linear models.
[[pdf]](https://arxiv.org/pdf/2008.10150.pdf)
- Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu.
- Contrastive Learning Inverts the Data Generating Process.
[[pdf]](Contrastive Learning Inverts the Data Generating Process)
- Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel. ICML 2021
#### 2022
- Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions.
[[pdf]](https://arxiv.org/pdf/2103.03568.pdf)
- Jiaye Teng, Weiran Huang, and Haowei He. AISTATS 2022
#### 2023
- Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis.
[[pdf]](https://openreview.net/pdf?id=AjC0KBjiMu)
- Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison. ICLR 2023
- On the Stepwise Nature of Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/2303.15438)
- James B. Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht. ICML 2023
- What shapes the loss landscape of self supervised learning?
[[pdf]](https://openreview.net/pdf?id=3zSn48RUO8M)
- Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka. ICLR 2023
#### 2024
- Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses.
[[pdf]](https://arxiv.org/pdf/2405.18045)
[[code]](https://github.com/pakoromilas/DHEL-KCL)
- Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos, Mihalis Nicolaou, Yannis Panagakis. ICML 2024
- Matrix Information Theory for Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/2305.17326)
- Yifan Zhang, Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan. ICML 2024
- Information Flow in Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/2309.17281)
- Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan, Yifan Zhang. ICML 2024
Computer Vision
Survey
- Contrastive Representation Learning: A Framework and Review
[[pdf]](https://arxiv.org/abs/2010.05113)
- Phuc H. Le-Khac, Graham Healy, Alan F. Smeaton. IEEE Access 2020
- A Survey on Contrastive Self-supervised Learning
[[pdf]](https://arxiv.org/pdf/2011.00362.pdf)
- Ashish Jaiswal, Ashwin R Babu, Mohammad Z Zadeh, Debapriya Banerjee, Fillia Makedon
- Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey.
[[pdf]](https://arxiv.org/pdf/1902.06162.pdf)
- Longlong Jing and Yingli Tian. T-PAMI 2020
- Self-supervised Learning: Generative or Contrastive
[[pdf]](https://arxiv.org/pdf/2006.08218.pdf)
- Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, Jie Tang. TKDE 2021
- Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
[[pdf]](https://openreview.net/pdf?id=Ma25S4ludQ)
- Utku Ozbulak, Hyun Jung Lee, Beril Boga, Esla Timothy Anzaku, Ho-min Park, Arnout Van Messem, Wesley De Neve, Joris Vankerschaver. TMLR 2023
Image Representation Learning
#### Benchmark code
- FAIR Self-Supervision Benchmark [[pdf]](https://arxiv.org/abs/1905.01235) [[repo]](https://github.com/facebookresearch/fair_self_supervision_benchmark): various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.
- How Well Do Self-Supervised Models Transfer? [[pdf]](https://arxiv.org/abs/2011.13377) [[repo]](https://github.com/linusericsson/ssl-transfer): A benchmark for evaluating self-supervision consisting of many-shot/few-shot recognition, object detection, surface normal estimation and semantic segmentation.
#### 2015
- Unsupervised Visual Representation Learning by Context Prediction.
[[pdf]](https://arxiv.org/abs/1505.05192)
[[code]](http://graphics.cs.cmu.edu/projects/deepContext/)
- Doersch, Carl and Gupta, Abhinav and Efros, Alexei A. ICCV 2015
- Unsupervised Learning of Visual Representations using Videos.
[[pdf]](http://www.cs.cmu.edu/~xiaolonw/papers/unsupervised_video.pdf)
[[code]](http://www.cs.cmu.edu/~xiaolonw/unsupervise.html)
- Wang, Xiaolong and Gupta, Abhinav. ICCV 2015
- Learning to See by Moving.
[[pdf]](http://arxiv.org/abs/1505.01596)
[[code]](https://people.eecs.berkeley.edu/~pulkitag/lsm/lsm.html)
- Agrawal, Pulkit and Carreira, Joao and Malik, Jitendra. ICCV 2015
- Learning image representations tied to ego-motion.
[[pdf]](http://vision.cs.utexas.edu/projects/egoequiv/ijcv_bestpaper_specialissue_egoequiv.pdf)
[[code]](http://vision.cs.utexas.edu/projects/egoequiv/)
- Jayaraman, Dinesh and Grauman, Kristen. ICCV 2015
#### 2016
- Joint Unsupervised Learning of Deep Representations and Image Clusters.
[[pdf]](https://arxiv.org/pdf/1604.03628.pdf)
[[code-torch]](https://github.com/jwyang/JULE.torch)
[[code-caffe]](https://github.com/jwyang/JULE-Caffe)
- Jianwei Yang, Devi Parikh, Dhruv Batra. CVPR 2016
- Unsupervised Deep Embedding for Clustering Analysis.
[[pdf]](https://arxiv.org/pdf/1511.06335.pdf)
[[code]](https://github.com/piiswrong/dec)
- Junyuan Xie, Ross Girshick, and Ali Farhadi. ICML 2016
- Slow and steady feature analysis: higher order temporal coherence in video.
[[pdf]](http://vision.cs.utexas.edu/projects/slowsteady/cvpr16.pdf)
- Jayaraman, Dinesh and Grauman, Kristen. CVPR 2016
- Context Encoders: Feature Learning by Inpainting.
[[pdf]](https://people.eecs.berkeley.edu/~pathak/papers/cvpr16.pdf)
[[code]](https://people.eecs.berkeley.edu/~pathak/context_encoder/)
- Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A. CVPR 2016
- Colorful Image Colorization.
[[pdf]](https://arxiv.org/abs/1603.08511)
[[code]](http://richzhang.github.io/colorization/)
- Zhang, Richard and Isola, Phillip and Efros, Alexei A. ECCV 2016
- Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles.
[[pdf]](http://arxiv.org/abs/1603.09246)
[[code]](http://www.cvg.unibe.ch/research/JigsawPuzzleSolver.html)
- Noroozi, Mehdi and Favaro, Paolo. ECCV 2016
- Ambient Sound Provides Supervision for Visual Learning.
[[pdf]](http://arxiv.org/pdf/1608.07017)
[[code]](http://andrewowens.com/ambient/index.html)
- Owens, Andrew and Wu, Jiajun and McDermott, Josh and Freeman, William and Torralba, Antonio. ECCV 2016
- Learning Representations for Automatic Colorization.
[[pdf]](http://arxiv.org/pdf/1603.06668.pdf)
[[code]](http://people.cs.uchicago.edu/~larsson/colorization/)
- Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. ECCV 2016
- Unsupervised Visual Representation Learning by Graph-based Consistent Constraints.
[\[pdf\]](http://faculty.ucmerced.edu/mhyang/papers/eccv16_feature_learning.pdf)
[\[code\]](https://github.com/dongli12/FeatureLearning)
- Li, Dong and Hung, Wei-Chih and Huang, Jia-Bin and Wang, Shengjin and Ahuja, Narendra and Yang, Ming-Hsuan. ECCV 2016
#### 2017
- Adversarial Feature Learning.
[[pdf]](https://arxiv.org/pdf/1605.09782.pdf)
[[code]](https://github.com/jeffdonahue/bigan)
- Donahue, Jeff and Krahenbuhl, Philipp and Darrell, Trevor. ICLR 2017
- Self-supervised learning of visual features through embedding images into text topic spaces.
[[pdf]](https://arxiv.org/pdf/1705.08631.pdf)
[[code]](https://github.com/lluisgomez/TextTopicNet)
- L. Gomez and Y. Patel and M. RusiΓ±ol and D. Karatzas and C.V. Jawahar. CVPR 2017
- Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction.
[[pdf]](https://arxiv.org/abs/1611.09842)
[[code]](https://github.com/richzhang/splitbrainauto)
- Zhang, Richard and Isola, Phillip and Efros, Alexei A. CVPR 2017
- Learning Features by Watching Objects Move.
[[pdf]](https://people.eecs.berkeley.edu/~pathak/papers/cvpr17.pdf)
[[code]](https://people.eecs.berkeley.edu/~pathak/unsupervised_video/)
- Pathak, Deepak and Girshick, Ross and Dollar, Piotr and Darrell, Trevor and Hariharan, Bharath. CVPR 2017
- Colorization as a Proxy Task for Visual Understanding.
[[pdf]](http://arxiv.org/abs/1703.04044)
[[code]](http://people.cs.uchicago.edu/~larsson/color-proxy/)
- Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. CVPR 2017
- DeepPermNet: Visual Permutation Learning.
[\[pdf\]](https://arxiv.org/pdf/1704.02729.pdf)
[\[code\]](https://github.com/rfsantacruz/deep-perm-net)
- Cruz, Rodrigo Santa and Fernando, Basura and Cherian, Anoop and Gould, Stephen. CVPR 2017
- Unsupervised Learning by Predicting Noise.
[[pdf]](https://arxiv.org/abs/1704.05310)
[[code]](https://github.com/facebookresearch/noise-as-targets)
- Bojanowski, Piotr and Joulin, Armand. ICML 2017
- Multi-task Self-Supervised Visual Learning.
[[pdf]](https://arxiv.org/abs/1708.07860)
- Doersch, Carl and Zisserman, Andrew. ICCV 2017
- Representation Learning by Learning to Count.
[[pdf]](https://arxiv.org/abs/1708.06734)
- Noroozi, Mehdi and Pirsiavash, Hamed and Favaro, Paolo. ICCV 2017
- Transitive Invariance for Self-supervised Visual Representation Learning.
[[pdf]](https://arxiv.org/pdf/1708.02901.pdf)
- Wang, Xiaolong and He, Kaiming and Gupta, Abhinav. ICCV 2017
- Look, Listen and Learn.
[[pdf]](https://arxiv.org/pdf/1705.08168.pdf)
- Relja, Arandjelovic and Zisserman, Andrew. ICCV 2017
- Unsupervised Representation Learning by Sorting Sequences.
[[pdf]](https://arxiv.org/pdf/1708.01246.pdf)
[[code]](https://github.com/HsinYingLee/OPN)
- Hsin-Ying Lee, Jia-Bin Huang, Maneesh Kumar Singh, and Ming-Hsuan Yang. ICCV 2017
#### 2018
- Unsupervised Feature Learning via Non-parameteric Instance Discrimination
[[pdf]](https://arxiv.org/pdf/1805.01978.pdf)
[[code]](https://github.com/zhirongw/lemniscate.pytorch)
- Zhirong Wu, Yuanjun Xiong and X Yu Stella and Dahua Lin. CVPR 2018
- Learning Image Representations by Completing Damaged Jigsaw Puzzles.
[[pdf]](https://arxiv.org/pdf/1802.01880.pdf)
[[code]](https://github.com/MehdiNoroozi/JigsawPuzzleSolver)
- Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. WACV 2018
- Unsupervised Representation Learning by Predicting Image Rotations.
[[pdf]](https://openreview.net/forum?id=S1v4N2l0-)
[[code]](https://github.com/gidariss/FeatureLearningRotNet)
- Spyros Gidaris and Praveer Singh and Nikos Komodakis. ICLR 2018
- Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization.
[[pdf]](https://openreview.net/pdf?id=HkMvEOlAb)
[[code]](https://github.com/ozcell/LALNets)
- Ozsel Kilinc and Ismail Uysal. ICLR 2018
- Improvements to context based self-supervised learning.
[[pdf]](https://arxiv.org/abs/1711.06379)
- Terrell Mundhenk and Daniel Ho and Barry Chen. CVPR 2018
- Self-Supervised Feature Learning by Learning to Spot Artifacts.
[[pdf]](https://arxiv.org/pdf/1806.05024.pdf)
[[code]](https://github.com/sjenni/LearningToSpotArtifacts)
- Simon Jenni and UniversitΓ€t Bern and Paolo Favaro. CVPR 2018
- Boosting Self-Supervised Learning via Knowledge Transfer.
[[pdf]](https://www.csee.umbc.edu/~hpirsiav/papers/transfer_cvpr18.pdf)
- Mehdi Noroozi and Ananth Vinjimoor and Paolo Favaro and Hamed Pirsiavash. CVPR 2018
- Cross-domain Self-supervised Multi-task Feature Learning Using Synthetic Imagery.
[[pdf]](https://arxiv.org/abs/1711.09082)
[[code]](https://github.com/jason718/game-feature-learning)
- Zhongzheng Ren and Yong Jae Lee. CVPR 2018
- ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids.
[[pdf]](https://arxiv.org/pdf/1709.00505.pdf)
- Dinesh Jayaraman, UC Berkeley; Ruohan Gao, University of Texas at Austin; Kristen Grauman. ECCV 2018*
- Deep Clustering for Unsupervised Learning of Visual Features
[[pdf]](https://research.fb.com/wp-content/uploads/2018/09/Deep-Clustering-for-Unsupervised-Learning-of-Visual-Features.pdf)
[[code]](https://github.com/facebookresearch/deepcluster)
- Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze. ECCV 2018
- Cross Pixel Optical-Flow Similarity for Self-Supervised Learning.
[[pdf]](http://www.robots.ox.ac.uk/~vgg/publications/2018/Mahendran18/mahendran18.pdf)
- Aravindh Mahendran, James Thewlis, Andrea Vedaldi. ACCV 2018
#### 2019
- Representation Learning with Contrastive Predictive Coding.
[[pdf]](https://arxiv.org/abs/1807.03748)
- Aaron van den Oord, Yazhe Li, Oriol Vinyals.
- Self-Supervised Learning via Conditional Motion Propagation.
[[pdf]](<https://arxiv.org/abs/1903.11412>)
[[code]](https://github.com/XiaohangZhan/conditional-motion-propagation)
- Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, and Chen Change Loy. CVPR 2019
- Self-Supervised Representation Learning by Rotation Feature Decoupling.
[[pdf]](http://openaccess.thecvf.com/content_CVPR_2019/html/Feng_Self-Supervised_Representation_Learning_by_Rotation_Feature_Decoupling_CVPR_2019_paper.html)
[[code]](https://github.com/philiptheother/FeatureDecoupling)
- Zeyu Feng; Chang Xu; Dacheng Tao. CVPR 2019
- Revisiting Self-Supervised Visual Representation Learning.
[[pdf]](https://arxiv.org/abs/1901.09005)
[[code]](https://github.com/google/revisiting-self-supervised)
- Alexander Kolesnikov; Xiaohua Zhai; Lucas Beye. CVPR 2019
- Self-Supervised GANs via Auxiliary Rotation Loss.
[[pdf]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Self-Supervised_GANs_via_Auxiliary_Rotation_Loss_CVPR_2019_paper.pdf)
[[code]](https://github.com/vandit15/Self-Supervised-Gans-Pytorch)
- Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby. CVPR 2019
- AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data.
[[pdf]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_AET_vs._AED_Unsupervised_Representation_Learning_by_Auto-Encoding_Transformations_Rather_CVPR_2019_paper.pdf)
[[code]](https://github.com/maple-research-lab/AET)
- Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo. CVPR 2019
- Unsupervised Deep Learning by Neighbourhood Discovery.
[[pdf]](http://proceedings.mlr.press/v97/huang19b.html).
[[code]](https://github.com/Raymond-sci/AND).
- Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu. ICML 2019
- Contrastive Multiview Coding.
[[pdf]](https://arxiv.org/abs/1906.05849)
[[code]](https://github.com/HobbitLong/CMC/)
- Yonglong Tian and Dilip Krishnan and Phillip Isola.
- Large Scale Adversarial Representation Learning.
[[pdf]](https://arxiv.org/abs/1907.02544)
- Jeff Donahue, Karen Simonyan.
- Learning Representations by Maximizing Mutual Information Across Views.
[[pdf]](https://arxiv.org/pdf/1906.00910)
[[code]](https://github.com/Philip-Bachman/amdim-public)
- Philip Bachman, R Devon Hjelm, William Buchwalter
- Selfie: Self-supervised Pretraining for Image Embedding.
[[pdf]](https://arxiv.org/abs/1906.02940)
- Trieu H. Trinh, Minh-Thang Luong, Quoc V. Le
- Data-Efficient Image Recognition with Contrastive Predictive Coding
[[pdf]](https://arxiv.org/abs/1905.09272)
- Olivier J. He Μnaff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord
- Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
[[pdf]](https://arxiv.org/pdf/1906.12340)
[[code]](https://github.com/hendrycks/ss-ood)
- Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. NeurIPS 2019
- Boosting Few-Shot Visual Learning with Self-Supervision
[[pdf]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Gidaris_Boosting_Few-Shot_Visual_Learning_With_Self-Supervision_ICCV_2019_paper.pdf)
- Pyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick PΓ©rez, and Matthieu Cord. ICCV 2019
- Self-Supervised Generalisation with Meta Auxiliary Learning
[[pdf]](https://arxiv.org/pdf/1901.08933.pdf)
[[code]](https://github.com/lorenmt/maxl)
- Shikun Liu, Andrew J. Davison, Edward Johns. NeurIPS 2019
- Wasserstein Dependency Measure for Representation Learning
[[pdf]](https://arxiv.org/pdf/1903.11780.pdf)
[[code]](https://github.com/SeongokRyu/mutual_information_and_self-supervised_learning/tree/master/predictive_coding)
- Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet. NeurIPS 2019
- Scaling and Benchmarking Self-Supervised Visual Representation Learning
[[pdf]](https://arxiv.org/abs/1905.01235)
[[code]](https://github.com/facebookresearch/fair_self_supervision_benchmark)
- Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra. ICCV 2019
- Unsupervised Pre-Training of Image Features on Non-Curated Data
[[pdf]](https://arxiv.org/pdf/1905.01278.pdf)
[[code]](https://github.com/facebookresearch/DeeperCluster)
- Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin. ICCV 2019 Oral
- S4L: Self-Supervised Semi-Supervised Learning
[[pdf]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhai_S4L_Self-Supervised_Semi-Supervised_Learning_ICCV_2019_paper.pdf)
[[code]](https://github.com/google-research/s4l)
- Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer. ICCV 2019
- Self-supervised model adaptation for multimodal semantic segmentation.
[[pdf]](https://arxiv.org/abs/1808.03833)
[[code]](https://github.com/DeepSceneSeg/SSMA)
- Abhinav Valada, Rohit Mohan, and Wolfram Burgard. IJCV 2019
#### 2020
- A critical analysis of self-supervision, or what we can learn from a single image
[[pdf]](https://arxiv.org/pdf/1904.13132)
[[code]](https://github.com/yukimasano/linear-probes)
- Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi. ICLR 2020
- On Mutual Information Maximization for Representation Learning
[[pdf]](https://arxiv.org/pdf/1907.13625.pdf)
[[code]](https://github.com/google-research/google-research/tree/master/mutual_information_representation_learning)
- Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic. ICLR 2020
- Understanding the Limitations of Variational Mutual Information Estimators
[[pdf]](https://arxiv.org/pdf/1910.06222)
[[code]](https://github.com/ermongroup/smile-mi-estimator)
- Jiaming Song, Stefano Ermon. ICLR 2020
- Self-labelling via simultaneous clustering and representation learning
[[pdf]](https://openreview.net/pdf?id=Hyx-jyBFPr)
[[blogpost]](http://www.robots.ox.ac.uk/~vgg/blog/self-labelling-via-simultaneous-clustering-and-representation-learning.html)
[[code]](https://github.com/yukimasano/self-label)
- Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi. ICLR 2020 (Spotlight)
- Self-supervised Label Augmentation via Input Transformations
[[pdf]](https://arxiv.org/abs/1910.05872)
[[code]](https://github.com/hankook/SLA)
- Hankook Lee, Sung Ju Hwang, Jinwoo Shin. ICML 2020
- Automatic Shortcut Removal for Self-Supervised Representation Learning
[[pdf]](https://arxiv.org/pdf/2002.08822.pdf)
- Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
- A Simple Framework for Contrastive Learning of Visual Representations
[[pdf]](https://arxiv.org/abs/2002.05709)
[[code]](https://github.com/google-research/simclr)
- Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton. ICML 2020
- How Useful is Self-Supervised Pretraining for Visual Tasks?
[[pdf]](https://arxiv.org/abs/2003.14323)
[[code]](https://github.com/princeton-vl/selfstudy-render)
- Alejandro Newell, Jia Deng. CVPR 2020
- Momentum Contrast for Unsupervised Visual Representation Learning
[[pdf]](https://arxiv.org/pdf/1911.05722.pdf)
[code]
- Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick. CVPR 2020
- ClusterFit: Improving Generalization of Visual Representations
[[pdf]](https://arxiv.org/abs/1912.03330)
- Xueting Yan, Ishan Misra, Abhinav Gupta, Deepti Ghadiyaram, Dhruv Mahajan. CVPR 2020
- Self-Supervised Learning of Pretext-Invariant Representations
[[pdf]](https://arxiv.org/abs/1912.01991)
- Ishan Misra, Laurens van der Maaten. CVPR 2020
- Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
[[pdf]](https://arxiv.org/abs/2006.07733)
[[unofficial-code]](https://github.com/lucidrains/byol-pytorch)
- Jean-Bastien Grill, Florian Strub, Florent AltchΓ©, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, RΓ©mi Munos, Michal Valko. NeurIPS 2020, Oral
- Contrastive learning of global and local features for medical image segmentation with limited annotations
[[pdf]](https://arxiv.org/pdf/2006.10511.pdf)
[[code]](https://github.com/krishnabits001/domain_specific_cl)
- Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu. NeurIPS 2020, Oral
- Unsupervised Representation Learning by InvariancePropagation
[[pdf]](https://arxiv.org/pdf/2010.11694.pdf)
[[code]](https://github.com/WangFeng18/InvariancePropagation)
- Feng Wang, Huaping Liu, Di Guo, Fuchun Sun. NeurIPS 2020, Spotlight
- Big Self-Supervised Models are Strong Semi-Supervised Learners
[[pdf]](https://arxiv.org/abs/2006.10029)
[[code]](https://github.com/google-research/simclr)
- Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton. NeurIPS 2020
- Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
[[pdf]](https://arxiv.org/pdf/2006.11325.pdf)
[[code]](https://github.com/indy-lab/ProtoTransfer)
- Carlos Medina, Arnout Devos, Matthias Grossglauser
- SCAN: Learning to Classify Images without Labels
[[pdf]](https://arxiv.org/abs/2005.12320)
[[code]](https://github.com/wvangansbeke/Unsupervised-Classification)
- Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool. ECCV 2020
- Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
[[pdf]](https://arxiv.org/abs/2006.09882)
[[code]](https://github.com/facebookresearch/swav)
- Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin. NeurIPS 2020
- Self-Supervised Relational Reasoning for Representation Learning
[[pdf]](https://arxiv.org/pdf/2006.05849.pdf)
[[code]](https://github.com/mpatacchiola/self-supervised-relational-reasoning)
- Massimiliano Patacchiola, Amos Storkey. NeurIPS 2020, Spotlight
- Exploring Simple Siamese Representation Learning
[[pdf]](https://arxiv.org/pdf/2011.10566)
[[unofficial-code]](https://github.com/PatrickHua/SimSiam)
- Xinlei Chen, Kaiming He
- Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning
[[pdf]](https://arxiv.org/pdf/2012.11552)
[[code]](https://github.com/valeoai/obow)
- Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, Patrick PΓ©rez
- Rethinking the Value of Labels for Improving Class-Imbalanced Learning
[[pdf]](https://arxiv.org/abs/2006.07529)
[[code]](https://github.com/YyzHarry/imbalanced-semi-self)
- Yuzhe Yang, Zhi Xu. NeurIPS 2020
- Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
[[pdf]](https://arxiv.org/pdf/2007.13916.pdf)
- Senthil Purushwalkam, Abhinav Gupta. NeurIPS 2020
- Mitigating embedding and class assignment mismatch in unsupervised image classification
[[pdf]](https://link.springer.com/chapter/10.1007/978-3-030-58586-0_45)
[[code]](https://github.com/Sungwon-Han/TwoStageUC)
- Sungwon Han, Sungwon Park, Sungkyu Park, Sundong Kim, Meeyoung Cha. ECCV 2020
#### 2021
- Self-Supervised Learning Across Domains
[[pdf]](https://arxiv.org/abs/2007.12368)
- Silvia Bucci, Antonio D'Innocente, Yujun Liao, Fabio Maria Carlucci, Barbara Caputo, Tatiana Tommasi. T-PAMI 2021
- Barlow twins: Self-supervised learning via redundancy reduction
[[pdf]](https://arxiv.org/abs/2103.03230)
[[code]](https://github.com/facebookresearch/barlowtwins)
- Zbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S.
- Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation
[[pdf]](https://arxiv.org/abs/2106.06801)
- Prashant Pandey, Ajey Pai, Nisarg Bhatt, Prasenjit Das, Govind Makharia, Prathosh AP, Mausam. MICCAI 2021
- Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
[[pdf]](https://arxiv.org/pdf/2011.10043)
[[code]](https://github.com/zdaxie/PixPro)
- Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu. CVPR 2021
- How Well Do Self-Supervised Models Transfer?
[[pdf]](https://arxiv.org/abs/2011.13377)
[[code]](https://github.com/linusericsson/ssl-transfer)
- Linus Ericsson, Henry Gouk, Timothy M. Hospedales. CVPR 2021
- Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting.
[[code]](https://github.com/AyanKumarBhunia/Self-Supervised-Learning-for-Sketch)
- Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song. CVPR 2021
- SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning
[[pdf]](https://arxiv.org/abs/2009.07724)
[[code]](https://github.com/cjrd/selfaugment)
- Colorado Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, Kurt Keutzer. CVPR 2021
- Jigsaw Clustering for Unsupervised Visual Representation Learning
[[pdf]](https://arxiv.org/abs/2104.00323)
[[code]](https://github.com/dvlab-research/JigsawClustering)
- Pengguang Chen, Shu Liu, Jiaya Jia. CVPR 2021
- Improving Unsupervised Image Clustering With Robust Learning
[[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.pdf)
[[code]](https://github.com/deu30303/RUC)
- Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha. CVPR 2021
- Improving Contrastive Learning by Visualizing Feature Transformation
[[pdf]](https://arxiv.org/abs/2108.02982)
[[code]](https://github.com/DTennant/CL-Visualizing-Feature-Transformation)
- Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen. ICCV 2021 Oral
#### 2022
- Tailoring Self-Supervision for Supervised Learning
[[pdf]](https://arxiv.org/abs/2207.10023)
[[code]](https://github.com/wjun0830/Localizable-Rotation)
- WonJun Moon, Ji-Hwan Kim, Jae-Pil Heo. ECCV 2022
- FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
[[pdf]](https://arxiv.org/abs/2207.09158)
[[code]](https://github.com/Sungwon-Han/FEDX)
- Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xing Xie, Meeyoung Cha. ECCV 2022
- Masked Siamese Networks for Label-Efficient Learning
[[pdf]](https://arxiv.org/abs/2204.07141)
[[code]](https://github.com/facebookresearch/msn)
- Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
- TriBYOL: Triplet BYOL for Self-Supervised Representation Learning
[[pdf]](https://arxiv.org/abs/2206.03012)
- Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama. ICASSP 2022
- Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
[[pdf]](https://arxiv.org/abs/2206.03009)
- Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama. ICASSP 2022
- Adaptive Soft Contrastive Learning
[[pdf]](https://arxiv.org/abs/2207.11163)
[[code]](https://github.com/MrChenFeng/ASCL_ICPR2022)
- Chen Feng, Ioannis Patras. ICPR 2022
- Self-Supervised Visual Representation Learning with Semantic Grouping
[[pdf]](https://arxiv.org/abs/2205.15288)
[[code]](https://github.com/CVMI-Lab/SlotCon)
- Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, and Xiaojuan Qi. NeurIPS 2022
- VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
[[pdf]](https://arxiv.org/abs/2105.04906)
- Adrien Bardes, Jean Ponce, Yann LeCun. ICLR 2022
#### 2023
- Inter-Instance Similarity Modeling for Contrastive Learning
[[pdf]](https://arxiv.org/abs/2306.12243)
[[code]](https://github.com/visresearch/patchmix)
- Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang.
- Asymmetric Patch Sampling for Contrastive Learning
[[pdf]](https://arxiv.org/abs/2306.02854)
[[code]](https://github.com/visresearch/aps)
- Chengchao Shen, Jianzhong Chen, Shu Wang, Hulin Kuang, Jin Liu, Jianxin Wang.
#### 2024
- Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning
[[pdf]](https://arxiv.org/pdf/2406.14525)
[[code]](https://github.com/cdvetal/evodenss)
- Adriano Vinhas, JoΓ£o Correia, Penousal Machado. CEC 2024
Video Representation Learning
- Unsupervised Learning of Video Representations using LSTMs.
[[pdf]](https://arxiv.org/pdf/1502.04681.pdf)
[[code]](https://github.com/emansim/unsupervised-videos)
- Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan. ICML 2015
- Shuffle and Learn: Unsupervised Learning using Temporal Order Verification.
[[pdf]](http://arxiv.org/abs/1603.08561)
[[code]](https://github.com/imisra/shuffle-tuple)
- Ishan Misra, C. Lawrence Zitnick and Martial Hebert. ECCV 2016
- LSTM Self-Supervision for Detailed Behavior Analysis
[[pdf]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Brattoli_LSTM_Self-Supervision_for_CVPR_2017_paper.pdf)
- Biagio Brattoli, Uta BΓΌchler, Anna-Sophia Wahl, Martin E. Schwab, and BjΓΆrn Ommer. CVPR 2017
- Self-Supervised Video Representation Learning With Odd-One-Out Networks.
[[pdf]](https://arxiv.org/abs/1611.06646)
- Basura Fernando and Hakan Bilen and Efstratios Gavves and Stephen Gould. CVPR 2017
- Unsupervised Learning of Long-Term Motion Dynamics for Videos.
[[pdf]](https://arxiv.org/pdf/1701.01821.pdf)
- Luo, Zelun and Peng, Boya and Huang, De-An and Alahi, Alexandre and Fei-Fei, Li. CVPR 2017
- Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning.
[[pdf]](http://ai.ucsd.edu/~haosu/papers/cvpr18_geometry_predictive_learning.pdf)
- Chuang Gan and Boqing Gong and Kun Liu and Hao Su and Leonidas J. Guibas. CVPR 2018
- Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning.
[[pdf]](https://arxiv.org/abs/1807.11293)
- Biagio Brattoli, Uta BΓΌchler, and BjΓΆrn Ommer. ECCV 2018
- Self-supervised learning of a facial attribute embedding from video.
[[pdf]](http://www.robots.ox.ac.uk/~vgg/publications/2018/Wiles18a/wiles18a.pdf)
- Wiles, O., Koepke, A.S., Zisserman, A. BMVC 2018
- Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles.
[[pdf]](https://arxiv.org/pdf/1811.09795.pdf)
- Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. AAAI 2019
- Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics.
[[pdf]](https://arxiv.org/abs/1904.03597)
- Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019
- DynamoNet: Dynamic Action and Motion Network.
[[pdf]](https://arxiv.org/pdf/1904.11407.pdf)
- Ali Diba; Vivek Sharma, Luc Van Gool, Rainer Stiefelhagen. ICCV 2019
- Learning Correspondence from the Cycle-consistency of Time.
[[pdf]](https://arxiv.org/abs/1903.07593)
[[code]](https://github.com/xiaolonw/TimeCycle)
- Xiaolong Wang, Allan Jabri and Alexei A. Efros. CVPR 2019
- Joint-task Self-supervised Learning for Temporal Correspondence.
[[pdf]](https://arxiv.org/abs/1909.11895)
[[code]](https://github.com/Liusifei/UVC)
- Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang. NIPS 2019
- Self-Supervised Video Representation Learning Using Inter-Intra Contrstive Framework
[[pdf]](https://arxiv.org/pdf/2008.02531.pdf)
[[code]](https://github.com/BestJuly/IIC)
- Li Tao, Xueting Wang, Toshihiko Yamasaki. ACMMM 2020*
- Video Playback Rate Perception for Self-Supervised Spatio-Temporal Representation Learning
[[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yao_Video_Playback_Rate_Perception_for_Self-Supervised_Spatio-Temporal_Representation_Learning_CVPR_2020_paper.pdf)
[[Code]](https://github.com/yuanyao366/PRP)
- Yuan Yao, Chang Liu, Dezhao Luo, Yu Zhou, Qixiang Ye. CVPR 2020
- Self-Supervised Video Representation Learning by Pace Prediction
[[pdf]](http://www.robots.ox.ac.uk/~vgg/publications/2020/Wang20/wang20.pdf)
[[code]](https://github.com/laura-wang/video-pace)
- Jiangliu Wang, Jianbo Jiao, Yun-Hui Liu. ECCV 2020
- Video Representation Learning by Recognizing Temporal Transformations
[[pdf]](https://arxiv.org/pdf/2007.10730.pdf)
[[code]](https://github.com/sjenni/temporal-ssl)
- Simon Jenni, Givi Meishvili, Paolo Favaro. ECCV 2020
- Self-supervised Co-training for Video Representation Learning
[[pdf]](https://arxiv.org/pdf/2010.09709)
[[code]](https://github.com/TengdaHan/CoCLR)
- Tengda Han, Weidi Xie, and Andrew Zisserman. NeurIPS 2020
- Cycle-Contrast for Self-Supervised Video Representation Learning
[[pdf]](https://arxiv.org/pdf/2010.14810)
- Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, and Tomokazu Murakami. NeurIPS 2020
- Video Representation Learning with Visual Tempo Consistency
[[pdf]](https://arxiv.org/pdf/2006.15489)
[[code]](https://github.com/decisionforce/VTHCL)
- Ceyuan Yang, Yinghao Xu, Bo Dai, and Bolei Zhou
- Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
[[pdf]](https://arxiv.org/pdf/2008.13426)
- Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Wei Liu, and Yun-hui Liu
- Spatiotemporal Contrastive Video Representation Learning
[[pdf]](https://arxiv.org/pdf/2008.03800)
- Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui
- Self-Supervised Video Representation Using Pretext-Contrastive Learning
[[pdf]](https://arxiv.org/pdf/2010.15464)
- Li Tao, Xueting Wang, and Toshihiko Yamasaki
- Unsupervised Video Representation Learning by Bidirectional Feature Prediction
[[pdf]](https://arxiv.org/pdf/2011.06037)
- Nadine Behrmann, Juergen Gall, and Mehdi Noroozi
- RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
[[pdf]](https://arxiv.org/pdf/2011.07949)
[[code]](https://github.com/PeihaoChen/RSPNet)
- Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, and Chuang Gan. AAAI 2021
- Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning
[[pdf]](https://arxiv.org/pdf/2011.11261)
- Zehua Zhang and David Crandall
- Can Temporal Information Help with Contrastive Self-Supervised Learning?
[[pdf]](https://arxiv.org/pdf/2011.13046)
- Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, Vikas Chandra, and Alan Yuille
- Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
[[pdf]](https://arxiv.org/pdf/2009.05757)
[[code]](https://github.com/FingerRec/DSM-decoupling-scene-motion)
- Jinpeng Wang, Yuting Gao, Ke Li, Jianguo Hu, Xinyang Jiang, Xiaowei Guo, Rongrong Ji, and Xing Sun. AAAI 2021
- Space-Time Correspondence as a Contrastive Random Walk
[[pdf]](https://arxiv.org/abs/2006.14613)
[[code]](https://github.com/ajabri/videowalk/)
[[project]](http://ajabri.github.io/videowalk)
- Allan Jabri, Andrew Owens, Alexei A. Efros. NeurIPS 2020 Oral
#### Benchmark code for video self-supervised learning
- How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
[[pdf]](https://arxiv.org/abs/2203.14221)
[[code]](https://github.com/fmthoker/SEVERE-BENCHMARK)
- Thoker, Fida Mohammad and Doughty, Hazel and Bagad, Piyush and Snoek, Cees . ECCV 2022
3D Feature Learning
- Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
[[pdf]](http://papers.neurips.cc/paper/9455-self-supervised-deep-learning-on-point-clouds-by-reconstructing-space.pdf)
- Jonathan Sauder, and Bjarne Sievers NeurIPS 2019
- Self-Supervised Learning of Point Clouds via Orientation Estimation
[[pdf]](http://www.vovakim.com/papers/20_3DV_RotationSupervision.pdf)
[[code]](https://github.com/OmidPoursaeed/Self_supervised_Learning_Point_Clouds)
- Omid Poursaeed, Tianxing Jiang, Han Qiao, Nayun Xu, and Vladimir G. Kim,3DV 2020
- Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models
[[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Eckart_Self-Supervised_Learning_on_3D_Point_Clouds_by_Learning_Discrete_Generative_CVPR_2021_paper.pdf)
- Benjamin Eckart, Wentao Yuan, Chao Liu, and Jan Kautz CVPR 2021
- PointContrast: Unsupervised Pre-training for 3D Point Cloud
[[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480579.pdf)
[[code]](https://github.com/facebookresearch/PointContrast)
- Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas Guibas, and Or Litany ECCV 2020
- Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
[[pdf]](https://arxiv.org/pdf/2110.08188)
- Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, and Jiaya Jia ICCV 2021
- Ponder: Point Cloud Pre-training via Neural Rendering
[[pdf]](https://openaccess.thecvf.com/content/ICCV2023/html/Huang_Ponder_Point_Cloud_Pre-training_via_Neural_Rendering_ICCV_2023_paper.html)
- Di Huang, Sida Peng, Tong He, Honghui Yang, Xiaowei Zhou and Wanli Ouyang ICCV 2023
- PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
[[pdf]](https://arxiv.org/abs/2310.08586)
[[code]](https://github.com/OpenGVLab/PonderV2)
- Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao and Wanli Ouyang Arxiv 2023
- UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
[[pdf]](https://arxiv.org/abs/2310.08586)
[[code]](https://arxiv.org/abs/2310.08370)
- Honghui Yang, Sha Zhang, Di Huang, Xiaoyang Wu, Haoyi Zhu, Tong He, Shixiang Tang, Hengshuang Zhao, Qibo Qiu, Binbin Lin, Xiaofei He, Wanli Ouyang Arxiv 2023
Geometry
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue.
[[pdf]](https://arxiv.org/pdf/1603.04992.pdf)
[[code]](https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation)
- Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, Ian Reid. ECCV 2016
- Self-supervised Learning of Motion Capture.
[[pdf]](https://arxiv.org/pdf/1712.01337.pdf)
[[code]](https://github.com/htung0101/3d_smpl)
[[web]](https://sites.google.com/view/selfsupervisedlearningofmotion/)
- Tung, Hsiao-Yu and Tung, Hsiao-Wei and Yumer, Ersin and Fragkiadaki, Katerina. NIPS 2017
- Unsupervised learning of object frames by dense equivariant image labelling.
[[pdf]](http://papers.neurips.cc/paper/6686-unsupervised-learning-of-object-frames-by-dense-equivariant-image-labelling.pdf)
- James Thewlis, Hakan Bilen, Andrea Vedaldi. NeurIPS 2017
- Unsupervised Learning of Depth and Ego-Motion from Video.
[[pdf]](https://arxiv.org/pdf/1704.07813.pdf)
[[code]](https://github.com/tinghuiz/SfMLearner)
[[web]](https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/)
- Zhou, Tinghui and Brown, Matthew and Snavely, Noah and Lowe, David G. CVPR 2017
- Active Stereo Net: End-to-End Self-Supervised Learning for Active Stereo Systems.
[[project]](http://asn.cs.princeton.edu/)
- Yinda Zhang, Sean Fanello, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Shahram Izadi, Thomas Funkhouser. ECCV 2018*
- Self-Supervised Relative Depth Learning for Urban Scene Understanding.
[[pdf]](https://people.cs.umass.edu/~hzjiang/files/ssr_depth.pdf)
[[project]](https://people.cs.umass.edu/~hzjiang/projects/ssr_depth/)
- Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich. ECCV 2018*
- Geometry-Aware Learning of Maps for Camera Localization.
[[pdf]](https://arxiv.org/abs/1712.03342)
[[code]](https://github.com/NVlabs/geomapnet)
- Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. CVPR 2018
- Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection.
[[pdf]](https://arxiv.org/abs/1804.01552)
[[web]](http://www.robots.ox.ac.uk/~vgg/research/probabilistic_introspection/)
- David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi. CVPR 2018
- Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry.
[[pdf]](https://arxiv.org/abs/1903.02330)
- Muhammed Kocabas; Salih Karagoz; Emre Akbas. CVPR 2019
- SelFlow: Self-Supervised Learning of Optical Flow.
[[pdf]](https://arxiv.org/abs/1904.09117)
- Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019
- Unsupervised Learning of Landmarks by Descriptor Vector Exchange.
[[pdf]](https://arxiv.org/abs/1908.06427)
[[code]](https://github.com/jamt9000/DVE)
[[web]](http://www.robots.ox.ac.uk/~vgg/research/DVE/)
- James Thewlis, Samuel Albanie, Hakan Bilen, Andrea Vedaldi. ICCV 2019
Audio
- Audio-Visual Scene Analysis with Self-Supervised Multisensory Features.
[[pdf]](https://arxiv.org/pdf/1804.03641.pdf)
[[code]](https://github.com/andrewowens/multisensory)
- Andrew Owens, Alexei A. Efros. ECCV 2018
- Objects that Sound.
[[pdf]](https://arxiv.org/pdf/1712.06651.pdf)
- R. ArandjeloviΔ, A. Zisserman. ECCV 2018
- Learning to Separate Object Sounds by Watching Unlabeled Video.
[[pdf]](https://arxiv.org/abs/1804.01665)
[[project]](http://vision.cs.utexas.edu/projects/separating_object_sounds/)
- Ruohan Gao, Rogerio Feris, Kristen Grauman. ECCV 2018
- The Sound of Pixels.
[[pdf]]( https://arxiv.org/pdf/1804.03160.pdf )
[[project]](https://github.com/hangzhaomit/Sound-of-Pixels)
- Zhao, Hang and Gan, Chuang and Rouditchenko, Andrew and Vondrick, Carl and McDermott, Josh and Torralba, Antonio. ECCV 2018
- Learnable PINs: Cross-Modal Embeddings for Person Identity.
[[pdf]](https://arxiv.org/abs/1805.00833)
[[web]](http://www.robots.ox.ac.uk/~vgg/research/LearnablePins/)
- Arsha Nagrani, Samuel Albanie, Andrew Zisserman. ECCV 2018
- Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization.
[[pdf]](http://papers.nips.cc/paper/8002-cooperative-learning-of-audio-and-video-models-from-self-supervised-synchronization.pdf)
- Bruno Korbar,Dartmouth College, Du Tran, Lorenzo Torresani. NIPS 2018
- Self-Supervised Generation of Spatial Audio for 360Β° Video.
[[pdf]](http://papers.nips.cc/paper/7319-self-supervised-generation-of-spatial-audio-for-360-video.pdf)
- Pedro Morgado, Nuno Nvasconcelos, Timothy Langlois, Oliver Wang. NIPS 2018
- TriCycle: Audio Representation Learning from Sensor Network Data Using Self-Supervision
[[pdf]](http://www.justinsalamon.com/uploads/4/3/9/4/4394963/cartwright_tricycle_waspaa2019.pdf)
- Mark Cartwright, Jason Cramer, Justin Salamon, Juan Pablo Bello. WASPAA 2019
- Self-supervised audio-visual co-segmentation
[[pdf]](https://arxiv.org/pdf/1904.09013.pdf)
- Andrew Rouditchenko, Hang Zhao, Chuang Gan, Josh McDermott, and Antonio Torralba. ICASSP 2019
- Does Visual Self-Supervision Improve Learning of Speech Representations?
[[pdf]](https://arxiv.org/pdf/2005.01400.pdf)
- Abhinav Shukla, Stavros Petridis, Maja Pantic
- There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge
[[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Valverde_There_Is_More_Than_Meets_the_Eye_Self-Supervised_Multi-Object_Detection_CVPR_2021_paper.pdf)
[[code]](https://github.com/robot-learning-freiburg/MM-DistillNet)
- Francisco Rivera Valverde, Juana Valeria Hurtado, and Abhinav Valada. CVPR 2021
- BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation.
[[pdf]](https://arxiv.org/pdf/2103.06695.pdf)
[[code]](https://github.com/nttcslab/byol-a)
- Daisuke Niizumi; Daiki Takeuchi; Yasunori Ohishi IJCNN 2021
- Learning State-Aware Visual Representations from Audible Interactions
[[pdf]](https://arxiv.org/abs/2209.13583)
[[code]](https://github.com/HimangiM/RepLAI)
- Himangi Mittal, Pedro Morgado, Unnat Jain, Abhinav Gupta. NeurIPS 2022
Others
- Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
[[pdf]](https://arxiv.org/abs/2206.02909)
[[code]](https://github.com/OxWearables/ssl-wearables)
- Hang Yuan, Shing Chan, Andrew P. Creagh, Catherine Tong, David A. Clifton, Aiden Doherty
- Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model.
[[pdf]](https://arxiv.org/abs/1611.07544)
- Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro. CVPR 2017
- Free Supervision from Video Games.
[[pdf]](http://www.philkr.net/papers/2018-06-01-cvpr/2018-06-01-cvpr.pdf)
[[project+code]](http://www.philkr.net/fsv/)
- Philipp KrΓ€henbΓΌhl. CVPR 2018
- Fighting Fake News: Image Splice Detection via Learned Self-Consistency
[[pdf]](https://arxiv.org/pdf/1805.04096.pdf)
[[code]](https://github.com/minyoungg/selfconsistency)
- Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros. ECCV 2018
- Self-supervised Tracking by Colorization (Tracking Emerges by Colorizing Videos).
[[pdf]](https://www.cs.columbia.edu/~vondrick//videocolor.pdf)
- Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy. ECCV 2018*
- High-Fidelity Image Generation With Fewer Labels.
[[pdf]](https://arxiv.org/pdf/1903.02271.pdf)
- Mario Lucic, Michael Tschannen, Marvin Ritter*, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly.
- Self-supervised Fitting of Articulated Meshes to Point Clouds.
- Chun-Liang Li, Tomas Simon, Jason Saragih, BarnabΓ‘s PΓ³czos and Yaser Sheikh. CVPR 2019
- Just Go with the Flow: Self-Supervised Scene Flow Estimation
[[pdf]](https://arxiv.org/pdf/1912.00497.pdf)
[[code]](https://github.com/HimangiM/Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation)
- Himangi Mittal, Brian Okorn, David Held. CVPR 2020
- SCOPS: Self-Supervised Co-Part Segmentation.
- Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz. CVPR 2019
- Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking.
- Jae Shin Yoon; Takaaki Shiratori; Shoou-I Yu; Hyun Soo Park. CVPR 2019
- Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations.
[[pdf]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Multi-Task_Self-Supervised_Object_Detection_via_Recycling_of_Bounding_Box_Annotations_CVPR_2019_paper.pdf)
[[code]](https://github.com/wonheeML/mtl-ssl)
- Wonhee Lee; Joonil Na; Gunhee Kim. CVPR 2019
- Self-Supervised Convolutional Subspace Clustering Network.
- Junjian Zhang; Chun-Guang Li; Chong You; Xianbiao Qi; Honggang Zhang; Jun Guo; Zhouchen Lin. CVPR 2019
- Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation.
- Xin Wang; Qiuyuan Huang; Asli Celikyilmaz; Jianfeng Gao; Dinghan Shen; Yuan-Fang Wang; William Yang Wang; Lei Zhang. CVPR 2019
- Unsupervised 3D Pose Estimation With Geometric Self-Supervision.
- Ching-Hang Chen; Ambrish Tyagi; Amit Agrawal; Dylan Drover; Rohith MV; Stefan Stojanov; James M. Rehg. CVPR 2019
- Learning to Generate Grounded Image Captions without Localization Supervision. [[pdf]](https://arxiv.org/pdf/1906.00283.pdf)
- Chih-Yao Ma; Yannis Kalantidis; Ghassan AlRegib; Peter Vajda; Marcus Rohrbach; Zsolt Kira.
- VideoBERT: A Joint Model for Video and Language Representation Learning [[pdf]](https://arxiv.org/pdf/1904.01766.pdf)
- Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid. ICCV 2019
- Countering Noisy Labels By Learning From Auxiliary Clean Labels [[pdf]]( https://arxiv.org/pdf/1905.13305.pdf )
- Tsung Wei Tsai, Chongxuan Li, Jun Zhu
- Self-Supervised Point Cloud Completion via Inpainting
[[pdf]](https://arxiv.org/abs/2111.10701)
- Himangi Mittal, Brian Okorn, Arpit Jangid, David Held. BMVC 2021
- ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
[[pdf]](https://arxiv.org/pdf/2202.00758.pdf)
[[code]](https://github.com/akhilmathurs/collossl)
- Yash Jain, Ian Tang, Chulhong Min, Fahim Kawsar, Akhil Mathur. UbiComp 2022
Machine Learning
- Self-taught Learning: Transfer Learning from Unlabeled Data.
[[pdf]](https://ai.stanford.edu/~hllee/icml07-selftaughtlearning.pdf)
- Raina, Rajat and Battle, Alexis and Lee, Honglak and Packer,
Benjamin and Ng, Andrew Y. ICML 2007
- Representation Learning: A Review and New Perspectives.
[[pdf]](https://arxiv.org/pdf/1206.5538.pdf)
- Bengio, Yoshua and Courville, Aaron and Vincent, Pascal. TPAMI 2013.
Reinforcement Learning
- Curiosity-driven Exploration by Self-supervised Prediction.
[[pdf]](http://pathak22.github.io/noreward-rl/resources/icml17.pdf)
[[code]](https://pathak22.github.io/noreward-rl/index.html#sourceCode)
- Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. ICML 2017
- Large-Scale Study of Curiosity-Driven Learning.
[[pdf]](https://pathak22.github.io/large-scale-curiosity/resources/largeScaleCuriosity2018.pdf)
- Yuri Burda, Harri Edwards, Deepak Pathak*, Amos Storkey, Trevor Darrell and Alexei A. Efros
- Playing hard exploration games by watching YouTube.
[[pdf]](https://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube.pdf)
- Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas. NIPS 2018
- Unsupervised State Representation Learning in Atari.
[[pdf]](https://arxiv.org/pdf/1906.08226.pdf)
[[code]](https://github.com/mila-iqia/atari-representation-learning)
- Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre CΓ΄tΓ©, R Devon Hjelm. NeurIPS 2019
- Visual Reinforcement Learning with Self-Supervised 3D Representations.
[[pdf]](https://arxiv.org/pdf/2210.07241.pdf)
[[code]](https://github.com/YanjieZe/rl3d)
- Yanjie Ze, Nicklas Hansen, Yinbo Chen, Mohit Jain, Xiaolong Wang. Preprint 2022
Recommendation Systems
- Self-supervised Learning for Deep Models in Recommendations.
[pdf]
- Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi (Jay)Kang, Evan Ettinger Preprint 2020
Robotics
2006
- Improving Robot Navigation Through Self-Supervised Online Learning
[[pdf]](http://www.roboticsproceedings.org/rss02/p04.pdf)
- Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, and Anthony Stentz
- Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation
[[pdf]](https://www.cs.ait.ac.th/~mdailey/cvreadings/Lookingbill-ReverseOptical.pdf)
- A. Lookingbill, D. Lieb, J. Rogers and J. Curry
2009
- Learning Long-Range Vision for Autonomous Off-Road Driving
[[pdf]](http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf)
- Raia Hadsell, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, Yann LeCun
2012
- Self-supervised terrain classification for planetary surface exploration rovers
[[pdf]](https://pdfs.semanticscholar.org/66b7/eef326d1db1fa2b19d5dc6b84d3d2a95b76c.pdf)
- Christopher A. Brooks, Karl Iagnemma
2014
- Terrain Traversability Analysis Using Multi-Sensor Data Correlation by a Mobile Robot
[[pdf]](http://sensor.eng.shizuoka.ac.jp/pdf/2014/SII.pdf)
- Mohammed Abdessamad Bekhti, Yuichi Kobayashi and Kazuki Matsumura
2015
- Online self-supervised learning for dynamic object segmentation
[[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.875.5829&rep=rep1&type=pdf)
- Vitor Guizilini and Fabio Ramos, The International Journal of Robotics Research
- Self-Supervised Online Learning of Basic Object Push Affordances
[[pdf]](http://abr.ijs.si/pdf/1429861734-RidgeIJARS2015.pdf)
- Barry Ridge, Ales Leonardis, Ales Ude, Miha Denisa, and Danijel Skocaj
- Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot
[[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139640)
- Tanis Mar, Vadim Tikhanoff, Giorgio Metta, and Lorenzo Natale
2016
- Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance
[[pdf]](https://arxiv.org/pdf/1603.08047.pdf)
- Kevin van Hecke, Guido de Croon, Laurens van der Maaten, Daniel Hennes, and Dario Izzo
- The Curious Robot: Learning Visual Representations via Physical Interactions.
[[pdf]](https://arxiv.org/pdf/1604.01360v2)
- Lerrel Pinto and Dhiraj Gandhi and Yuanfeng Han and Yong-Lae Park and Abhinav Gupta. ECCV 2016
- Learning to Poke by Poking: Experiential Learning of Intuitive Physics.
[\[pdf\]](https://arxiv.org/abs/1606.07419)
- Agrawal, Pulkit and Nair, Ashvin V and Abbeel, Pieter and Malik, Jitendra and Levine, Sergey. NIPS 2016
- Supersizing Self-supervision: Learning to Grasp from 50K Tries and
700 Robot Hours. [\[pdf\]](https://arxiv.org/pdf/1509.06825.pdf)
- Pinto, Lerrel and Gupta, Abhinav. ICRA 2016
2017
- Supervision via Competition: Robot Adversaries for Learning Tasks.
[[pdf]](https://arxiv.org/pdf/1610.01685.pdf)
- Pinto, Lerrel and Davidson, James and Gupta, Abhinav. ICRA 2017
- Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge.
[[pdf]](https://arxiv.org/pdf/1803.09956.pdf)
[[Project]](http://apc.cs.princeton.edu/)
- Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao. ICRA 2017
- Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation.
[[pdf]](https://arxiv.org/abs/1703.02018)
[[Project]](https://ropemanipulation.github.io/)
- Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine. ICRA 2017*
- Learning to Fly by Crashing
[[pdf]](https://arxiv.org/abs/1704.05588)
- Dhiraj Gandhi, Lerrel Pinto, Abhinav Gupta IROS 2017
- Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning
[[pdf]](http://www.esa.int/gsp/ACT/doc/AI/pub/ACT-RPR-AI-2017-ACTA-SSL.pdf)
- K. van Hecke, G. C. de Croon, D. Hennes, T. P. Setterfield, A. Saenz- Otero, and D. Izzo
- Unsupervised Perceptual Rewards for Imitation Learning.
[[pdf]](https://arxiv.org/abs/1612.06699)
[[project]](https://sermanet.github.io/rewards/)
- Sermanet, Pierre and Xu, Kelvin and Levine, Sergey. RSS 2017
- Self-Supervised Visual Planning with Temporal Skip Connections.
[[pdf]](http://arxiv.org/pdf/1710.05268)
- Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine. CoRL2017
2018
- CASSL: Curriculum Accelerated Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/1708.01354.pdf)
- Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta. ICRA 2018
- Time-Contrastive Networks: Self-Supervised Learning from Video.
[[pdf]](https://arxiv.org/pdf/1609.09475.pdf)
[[Project]](https://sermanet.github.io/imitate/)
- Pierre Sermanet and Corey Lynch and Yevgen Chebotar and Jasmine Hsu and Eric Jang and Stefan Schaal and Sergey Levine. ICRA 2018
- Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation.
[[pdf]](http://arxiv.org/pdf/1709.10489)
- Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine. ICRA 2018
- Learning Actionable Representations from Visual Observations.
[[pdf]](https://arxiv.org/pdf/1609.09475.pdf)
[[Project]](https://sermanet.github.io/imitate/)
- Dwibedi, Debidatta and Tompson, Jonathan and Lynch, Corey and Sermanet, Pierre. IROS 2018
- Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.
[[pdf]](https://arxiv.org/abs/1808.00928)
[[Project]](https://sites.google.com/view/actionablerepresentations/)
- Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser. IROS 2018
- Visual Reinforcement Learning with Imagined Goals.
[[pdf]](https://arxiv.org/abs/1807.04742)
[[Project]](https://sites.google.com/site/visualrlwithimaginedgoals/)
- Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine.NeurIPS 2018
- Grasp2Vec: Learning Object Representations from Self-Supervised Grasping.
[[pdf]](https://arxiv.org/pdf/1811.06964.pdf)
[[Project]](https://sites.google.com/site/grasp2vec/home)
- Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine. CoRL 2018
- Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning.
[[pdf]](https://arxiv.org/pdf/1810.03043.pdf)
[[Project]](https://sites.google.com/view/robustness-via-retrying)
- Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn. CoRL 2018
2019
- Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry.
[[pdf]](https://arxiv.org/abs/1809.07207)
- Mirko Nava, Jerome Guzzi, R. Omar Chavez-Garcia, Luca M. Gambardella, Alessandro Giusti. Robotics and Automation Letters
- Learning Latent Plans from Play.
[[pdf]](https://arxiv.org/pdf/1903.01973.pdf)
[[Project]](https://learning-from-play.github.io/)
- Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet
- Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning.
[[pdf]](https://arxiv.org/pdf/1912.03227.pdf)
- Jannik Zuern, Wolfram Burgard, Abhinav Valada
2020
- Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video.
[[pdf]](https://arxiv.org/pdf/1910.09430.pdf)
[[Project]](http://robotskills.cs.uni-freiburg.de/)
- Oier Mees, Markus Merklinger, Gabriel Kalweit, Wolfram Burgard ICRA 2020
2023
- Self-Supervised Object Goal Navigation with In-Situ Finetuning.
[[pdf]](https://arxiv.org/abs/2212.05923)
[[Video]](https://www.youtube.com/watch?v=LXsZst5ZUpU)
- So Yeon Min, Yao-Hung Hubert Tsai, Wei Ding, Ali Farhadi, Ruslan Salakhutdinov, Yonatan Bisk, Jian Zhang IROS 2023
2024
- Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning.
[[pdf]](https://arxiv.org/pdf/2402.02500.pdf)
- Haoyi Zhu, Yating Wang, Di Huang, Weicai Ye, Wanli Ouyang, Tong He
NLP
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
[[pdf]](https://arxiv.org/abs/1810.04805)
[[link]](https://github.com/google-research/bert)
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. NAACL 2019 Best Long Paper
- Self-Supervised Dialogue Learning
[[pdf]](https://arxiv.org/pdf/1907.00448.pdf)
- Jiawei Wu, Xin Wang, William Yang Wang. ACL 2019
- Self-Supervised Learning for Contextualized Extractive Summarization
[[pdf]](https://arxiv.org/pdf/1906.04466.pdf)
- Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang. ACL 2019
- A Mutual Information Maximization Perspective of Language Representation Learning
[[pdf]](https://openreview.net/pdf?id=Syx79eBKwr)
- Lingpeng Kong, Cyprien de Masson d'Autume, Lei Yu, Wang Ling, Zihang Dai, Dani Yogatama. ICLR 2020
- VL-BERT: Pre-training of Generic Visual-Linguistic Representations
[[pdf]](https://arxiv.org/pdf/1908.08530.pdf)
[[code]](https://github.com/jackroos/VL-BERT)
- Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai. ICLR 2020
- A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
[[pdf]](https://people.cs.vt.edu/~reddy/papers/AAAI21.pdf)
[[code]](https://github.com/tshi04/AspDecSSCL)
- Tian Shi, Liuqing Li, Ping Wang, and Chandan K. Reddy. AAAI 2021
- Self-Guided Contrastive Learning for BERT Sentence Representations
[[pdf]](https://arxiv.org/abs/2106.07345)
[[code]](https://github.com/galsang/SG-BERT)
- Taeuk Kim, Kang Min Yoo, and Sang-goo Lee. ACL 2021
ASR
- wav2vec: Unsupervised Pre-Training for Speech Recognition
[[pdf]](https://arxiv.org/pdf/1904.05862.pdf)
[[code]](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec)
- Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli. INTERSPEECH 2019
- Learning Robust and Multilingual Speech Representations
[[pdf]](https://arxiv.org/pdf/2001.11128.pdf)
- Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord. Findings of EMNLP 2020
- Unsupervised Pretraining Transfers Well Across Languages
[[pdf]](https://arxiv.org/pdf/2002.02848.pdf)
[[code]](https://github.com/facebookresearch/CPC_audio)
- Morgane Riviere, Armand Joulin, Pierre-Emmanuel Mazare, Emmanuel Dupoux. ICASSP 2020
- vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
[[pdf]](https://arxiv.org/pdf/1910.05453)
- Alexei Baevski, Steffen Schneider, Michael Auli. ICLR 2020
- Effectiveness of Self-supervised Pre-training for Speech Recognition
[[pdf]](https://arxiv.org/pdf/1911.03912.pdf)
- Alexei Baevski, Michael Auli, Abdelrahman Mohamed. ICASSP 2020
- Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning
[[pdf]](https://arxiv.org/pdf/1910.12729)
- Alexander H. Liu, Tao Tu, Hung-yi Lee, Lin-shan Lee. ICASSP 2020
- Self-Training for End-to-End Speech Recognition
[[pdf]](https://arxiv.org/pdf/1909.09116)
- Jacob Kahn, Ann Lee, Awni Hannun. ICASSP 2020
- Generative Pre-Training for Speech with Autoregressive Predictive Coding
[[pdf]](https://arxiv.org/pdf/1910.12607.pdf)
[[code]](https://github.com/iamyuanchung/Autoregressive-Predictive-Coding)
- Yu-An Chung, James Glass. ICASSP 2020
- Disentangled Speech Embeddings using Cross-modal Self-supervision
[[pdf]](https://arxiv.org/pdf/2002.08742v1.pdf)
- Arsha Nagrani, Joon Son Chung, Samuel Albanie, Andrew Zisserman. ICASSP 2020
- Multi-task Self-supervised Learning for Robust Speech Recognition
[[pdf]](https://arxiv.org/pdf/2001.09239.pdf)
- Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual, Pawel Swietojanski, Joao Monteiro, Jan Trmal, Yoshua Bengio. ICASSP 2020
- Visually Guided Self Supervised Learning of Speech Representations
[[pdf]](https://arxiv.org/pdf/2001.04316.pdf)
- Abhinav Shukla, Konstantinos Vougioukas, Pingchuan Ma, Stavros Petridis, Maja Pantic. ICASSP 2020
- Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
[[pdf]](https://arxiv.org/abs/1910.12638)
[[code]](https://github.com/s3prl/s3prl)
- Andy T. Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, Hung-yi Lee. ICASSP 2020
- Vector-Quantized Autoregressive Predictive Coding
[[pdf]](https://arxiv.org/abs/2005.08392)
[[code]](https://github.com/Alexander-H-Liu/NPC)
- Yu-An Chung, Hao Tang, James Glass. Interspeech 2020
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
[[pdf]](https://arxiv.org/abs/2006.11477)
[[code]](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec)
- Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael Auli. NeurIPS 2020
- Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
[[pdf]](https://arxiv.org/abs/2104.01027)
[[code]](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec)
- Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli
- HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
[[pdf]](https://arxiv.org/abs/2106.07447)
[[code]](https://github.com/pytorch/fairseq/tree/master/examples/hubert)
- Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. ICASSP 2021
- Unsupervised Speech Recognition
[[pdf]](https://arxiv.org/abs/2105.11084)
[[code]](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec/unsupervised)
- Alexei Baevski, Wei-Ning Hsu, Alexis Conneau, Michael Auli
- TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
[[pdf]](https://arxiv.org/abs/2007.06028)
[[code]](https://github.com/s3prl/s3prl)
- Andy T. Liu, Shang-Wen Li, Hung-yi Lee. IEEE/ACM TASLP 2021
- Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies
[[pdf]](https://arxiv.org/abs/2011.00406)
[[code]](https://github.com/Alexander-H-Liu/NPC)
- Alexander H. Liu, Yu-An Chung, James Glass. Interspeech 2021
Time-Series
- Unsupervised Scalable Representation Learning for Multivariate Time Series
[[pdf]](https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf)
[[code]](https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries)
- Franceschi, Jean-Yves, Aymeric Dieuleveut, and Martin Jaggi. NeurIPS 2019
- Time-Series Representation Learning via Temporal and Contextual Contrasting
[[pdf]](https://www.ijcai.org/proceedings/2021/0324.pdf)
[[code]](https://github.com/emadeldeen24/TS-TCC)
- Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. IJCAI 2021
- Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
[[pdf]](https://openreview.net/pdf?id=8qDwejCuCN)
[[code]](https://github.com/sanatonek/TNC_representation_learning)
- Tonekaboni, Sana, Danny Eytan, and Anna Goldenberg. ICLR 2021
- A Transformer-Based Framework for Multivariate Time Series Representation Learning
[[pdf]](https://arxiv.org/pdf/2010.02803.pdf)
[[code]](https://github.com/gzerveas/mvts_transformer)
- Zerveas, George, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff. KDD 2021
- TS2Vec: Towards Universal Representation of Time Series
[[pdf]](https://www.aaai.org/AAAI22Papers/AAAI-8809.YueZ.pdf)
[[code]](https://github.com/yuezhihan/ts2vec)
- Zerveas, George, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff. AAAI 2022
Graph
- Deep Graph Infomax
[[pdf]](https://openreview.net/forum?id=rklz9iAcKQ)
[[code]](https://github.com/PetarV-/DGI)
- Petar VeliΔkoviΔ, William Fedus, William L. Hamilton, Pietro LiΓ², Yoshua Bengio, R Devon Hjelm. ICLR 2019
- When Does Self-Supervision Help Graph Convolutional Networks
[[pdf]](https://arxiv.org/pdf/2006.09136.pdf)
- Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen. ICML 2020
- Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
[[pdf]](https://arxiv.org/pdf/1902.11038v2.pdf)
- Ke Sun, Zhouchen Lin, Zhanxing Zhu. AAAI 2020
- Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks
[[pdf]](https://arxiv.org/pdf/2006.12971v1.pdf)
- Arijit Sehanobish, Neal G. Ravindra, David van Dijk. ICML 2020 Workshop
- Deep Graph Contrastive Representation Learning
[[pdf]](http://arxiv.org/abs/2006.04131)
[[code]](https://github.com/CRIPAC-DIG/GRACE)
- Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. ICML 2020 Workshop
- Contrastive Multi-View Representation Learning on Graphs
[[pdf]](https://arxiv.org/pdf/2006.05582)
- Kaveh Hassani, Amir Hosein Khasahmadi. ICML 2020
- GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
[[pdf]](https://arxiv.org/pdf/2006.09963.pdf)
- Jiezhong Qiu, Qibin Chen, Yuxiao Dong. KDD 2020
- GPT-GNN: Generative Pre-Training of Graph Neural Networks
[[pdf]](https://arxiv.org/pdf/2006.15437.pdf)
[[code]](https://github.com/acbull/GPT-GNN)
- Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun. KDD 2020
- Self-supervised Learning on Graphs: Deep Insights and New Direction
[[pdf]](https://arxiv.org/pdf/2006.10141.pdf)
- Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang.
- Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
[[pdf]](https://people.cs.vt.edu/~reddy/papers/WWW21.pdf)
[[code]](https://github.com/pnnl/SLICE)
- Ping Wang, Khushbu Agarwal, Colby Ham, Sutanay Choudhury, and Chandan K. Reddy. WWW 2021
- Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
[[pdf]](https://people.cs.vt.edu/~reddy/papers/WWW21a.pdf)
[[code]](https://github.com/amazon-research/hyperbolic-embeddings)
- Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, and Chandan K. Reddy. WWW 2021
- GraphMAE: Self-supervised Masked Graph Autoencoders
[[pdf]](https://arxiv.org/pdf/2205.10803.pdf)
[[code]](https://github.com/THUDM/GraphMAE)
- Zhenyu Hou, Xiao Liu, Yukuo Ceng, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang. KDD 2022
Talks
- The power of Self-Learning Systems. Demis Hassabis (DeepMind).
[[link]](https://youtu.be/wxis9FrCHbw)
- Supersizing Self-Supervision: Learning Perception and Action without Human Supervision. Abhinav Gupta (CMU).
[[link]](https://simons.berkeley.edu/talks/abhinav-gupta-2017-3-28)
- Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder. Alyosha Efros (UCB)
[[link]](https://business.facebook.com/academics/videos/1632981350086599)
- Unsupervised Visual Learning Tutorial. CVPR 2018
[[part 1]](https://www.youtube.com/watch?v=gSqmUOAMwcc)
[[part 2]](https://www.youtube.com/watch?v=BijK_US6A0w)
- Self-Supervised Learning. Andrew Zisserman (Oxford & Deepmind).
[[pdf]](https://project.inria.fr/paiss/files/2018/07/zisserman-self-supervised.pdf)
- Graph Embeddings, Content Understanding, & Self-Supervised Learning. Yann LeCun. (NYU & FAIR)
[[pdf]](https://drive.google.com/file/d/12pDCno02FJPDEBk4iGuuaj8b2rr48Hh0/view)
[[video]](https://www.youtube.com/watch?v=UGPT64wo7lU)
- Self-supervised learning: could machines learn like humans? Yann LeCun @EPFL.
[[video]](https://www.youtube.com/watch?v=7I0Qt7GALVk)
- Week 9 (b): CS294-158 Deep Unsupervised Learning(Spring 2019). Alyosha Efros @UC Berkeley.
[[video]](https://www.youtube.com/watch?v=PX11C5Vfo9U)
Thesis
- Supervision Beyond Manual Annotations for Learning Visual Representations. Carl Doersch. [[pdf]](http://www.carldoersch.com/docs/thesis.pdf).
- Image Synthesis for Self-Supervised Visual Representation Learning. Richard Zhang. [[pdf]](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-36.pdf).
- Visual Learning beyond Direct Supervision. Tinghui Zhou. [[pdf]](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-128.pdf).
- Visual Learning with Minimal Human Supervision. Ishan Misra. [[pdf]](https://www.ri.cmu.edu/publications/visual-learning-with-minimal-human-supervision/).
Blog
- Self-Supervised Representation Learning. Lilian Weng. [[link]](https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html).
- Self Supervised Representation Learning in NLP. Amit Chaudhary. [[link]](https://amitness.com/2020/05/self-supervised-learning-nlp/).
- The Illustrated [[Self-Supervised Learning]](https://amitness.com/2020/02/illustrated-self-supervised-learning/), [[SimCLR]](https://amitness.com/2020/03/illustrated-simclr/), [[PIRL]](https://amitness.com/2020/03/illustrated-pirl/), [[Self-Labelling]](https://amitness.com/2020/04/illustrated-self-labelling/), [[FixMatch]](https://amitness.com/2020/03/fixmatch-semi-supervised/), [[DeepCluster]](https://amitness.com/2020/04/deepcluster/). Amit Chaudhary.
- Contrastive Self-Supervised Learning. Ankesh Anand. [[link]](https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html).
License
To the extent possible under law, Zhongzheng Ren has waived all copyright and related or neighboring rights to this work.
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