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https://github.com/dev-sungman/Awesome-Self-Supervised-Papers
Paper bank for Self-Supervised Learning
https://github.com/dev-sungman/Awesome-Self-Supervised-Papers
List: Awesome-Self-Supervised-Papers
awesome cvpr2020 self-supervised self-supervised-learning sota
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Paper bank for Self-Supervised Learning
- Host: GitHub
- URL: https://github.com/dev-sungman/Awesome-Self-Supervised-Papers
- Owner: dev-sungman
- Created: 2020-04-14T12:23:38.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-14T20:41:31.000Z (over 1 year ago)
- Last Synced: 2024-07-31T04:02:02.301Z (3 months ago)
- Topics: awesome, cvpr2020, self-supervised, self-supervised-learning, sota
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- Stars: 574
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- Forks: 59
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
Awesome-Self-Supervised-Papers
Collecting papers about Self-Supervised Learning, Representation Learning.
**Last Update : 2021. 09. 26.**
* Update papers that handles self-supervised learnning with distillation.
(Seed, Compress, DisCo, DoGo, SimDis ...)
* Add a dense prediction paper (SoCo)
**Any contributions, comments are welcome.**
# Computer Vision (CV)
## Pretraining / Feature / Representation
### Contrastive Learning
| Conference / Journal | Paper | ImageNet Acc (Top 1) |
| --------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
| CVPR 2006 | [Dimensionality Reduction by Learning an Invariant Mapping](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) | - |
| arXiv:1807.03748 | [Representation learning with contrastive predictive coding (CPC)](https://arxiv.org/pdf/1807.03748.pdf) | - |
| arXiv:1911.05722 | [Momentum Contrast for Unsupervised Visual Representation Learning (MoCo)](https://arxiv.org/pdf/1911.05722.pdf) | 60.6 % |
| arXiv:1905.09272 | [Data-Efficient Image Recognition contrastive predictive coding (CPC v2)](https://arxiv.org/pdf/1905.09272.pdf) | 63.8 % |
| arXiv:1906.05849 | [Contrastive Multiview Coding (CMC)](https://arxiv.org/pdf/1906.05849.pdf) | 66.2 % |
| arXiv:2002.05709 | [A Simple Framework for Contrastive Learning of Visual Representations (SimCLR)](https://arxiv.org/pdf/2002.05709.pdf) | 69.3 % |
| arXiv:2003.12338 | [Improved Baselines with Momentum Contrastive Learning(MoCo v2)](https://arxiv.org/pdf/2003.04297.pdf) | 71.1 % |
| arXiv:2003.05438 | [Rethinking Image Mixture for Unsupervised Visual Representation Learning](https://arxiv.org/pdf/2003.05438.pdf) | 65.9 % |
| arXiv:2004.05554 | [Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations](https://arxiv.org/pdf/2004.05554.pdf) | |
| arXiv:2006.10029 | [Big Self-Supervised Models are Strong Semi-Supervised Learners(SimCLRv2)](https://arxiv.org/pdf/2006.10029.pdf) | 77.5 % (10% label) |
| arXiv:2006.07733 | [Bootstrap Your Own Latent A New Approach to Self-Supervised Learning](https://arxiv.org/pdf/2006.07733.pdf) | 74.3 % |
| arXiv:2006.09882 | [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments(SwAV)](https://arxiv.org/pdf/2006.09882.pdf) | 75.3% |
| arXiv:2008.05659 | [What Should Not Be Contrastive in Contrastive Learning](https://arxiv.org/pdf/2008.05659.pdf) | 80.2 % (ImageNet-100) |
| arXiv:2007.00224 | [Debiased Contrastive Learning](https://arxiv.org/pdf/2007.00224.pdf) | 74.6 % (ImageNet-100) |
| arXiv:2009.00104 | [A Framework For Contrastive Self-Supervised Learning And Designing A New Approach](https://arxiv.org/pdf/2009.00104.pdf) | - |
| ICLR2021 under review | [SELF-SUPERVISED REPRESENTATION LEARNING VIA ADAPTIVE HARD-POSITIVE MINING](https://openreview.net/pdf?id=aLIbnLY9NtH) | 72.3% (ResNet-50(4x): 77.3%) |
| IEEE Access | [Contrastive Representation Learning: A Framework and Review](https://arxiv.org/pdf/2010.05113.pdf) | review paper |
| arXiv:2010.01929 | [EQCO: EQUIVALENT RULES FOR SELF-SUPERVISED CONTRASTIVE LEARNING](https://arxiv.org/pdf/2010.01929.pdf) | 68.5 % (Proposed) / 66.6 % (SimCLR) / 200epochs |
| arXiv:2010.01028 | [Hard Negative Mixing for Contrastive Learning](https://arxiv.org/pdf/2010.01028.pdf) | 68.0% / 200epochs |
| arXiv:2011.10566 | [Exploring Simple Siamese Representation Learning(SimSiam)](https://arxiv.org/pdf/2011.10566.pdf) | 68.1% / 100 epochs / 256 batch |
| arXiv:2010.06682 | [Are all negatives created equal in contrastive instance discrimination?](https://arxiv.org/pdf/2010.06682.pdf) | - |
| arXiv:2101.05224 | [Big Self-Supervised Models Advance Medical Image Classification](https://arxiv.org/pdf/2101.05224.pdf) | AUC: 0.7729 (SimCLR / ImagNet--> Chexpert / ResNet-152(2x)) |
| arXiv:2012.08850 | [Contrastive Learning Inverts the Data Generating Process](https://arxiv.org/pdf/2102.08850.pdf?fbclid=IwAR3sw-7IfgbgiIblQPb4Y1OCXKYn0dkcVtri_n8Ln73fgC2vSnPn9HYvi4E) | Theoretical fondation about contrastive learning |
| arXiv:2103.01988 | [Self-supervised Pretraining of Visual Features in the Wild](https://arxiv.org/pdf/2103.01988.pdf?fbclid=IwAR33zJ9CqxAdL3JJzo0eT2AkmdTpA9_zMc-566flGddOc1HMzESQTdu9piE) | (finetune) 83.8%(693M parameters), 84.2%(1.3B parameters) |
| arXiv:2103.03230 | [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://arxiv.org/pdf/2103.03230.pdf?fbclid=IwAR1p0f_DIdSF1gLFxcdAnBbUX7BO-5AFAqWQea0fNbOCsO5DK3TfQ6xTjo0) | 73.2% |
| arXiv:2104.02057 | [An Empirical Study of Training Self-Supervised Vision Transformers](https://arxiv.org/pdf/2104.02057.pdf) | 81.0% |
**Dense Contrastive Learning**
| Conference / Journal | Paper | AP(bbox) @COCO | AP(mask) @COCO |
| ---------------- | ------------------------------------------------------------ | --------------------- | ---------------- |
| NeurIPS 2020 | [Unsupervised Learning of Dense Visual Representations](https://arxiv.org/pdf/2011.05499.pdf) | 39.2 | 35.6 |
| arXiv:2011.09157 | [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://arxiv.org/pdf/2011.09157.pdf) | 40.3 @COCO | 36.4 |
| arXiv:2011.10043 | [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://arxiv.org/pdf/2011.10043.pdf) | 41.4 | 37.4 |
| arXiv:2102.08318 | [Instance Localization for Self-supervised Detection Pretraining](https://arxiv.org/pdf/2102.08318.pdf) | 42.0 | 37.6 |
| arXiv:2103.06122 | [Spatially Consistent Representation Learning](https://arxiv.org/pdf/2103.06122.pdf) | 41.3 | 37.7 |
| arXiv:2103.10957 | [Efficient Visual Pretraining with Contrastive Detection](https://arxiv.org/pdf/2103.10957.pdf) | 42.7 (DetCon_B) | 38.2 (DetCon_B) |
| arXiv:2106.02637 | [Aligning Pretraining for Detection via Object-Level Contrastive Learning](https://arxiv.org/pdf/2106.02637.pdf) | 43.2 | 38.4 |
### Image Transformation
| Conference / Journal | Paper | ImageNet Acc (Top 1). |
| -------------------- | ------------------------------------------------------------ | -------------------------- |
| ECCV 2016 | [Colorful image colorization(Colorization)](https://arxiv.org/pdf/1603.08511.pdf) | 39.6% |
| ECCV 2016 | [Unsupervised learning of visual representations by solving jigsaw puzzles](https://arxiv.org/pdf/1603.09246.pdf) | 45.7% |
| CVPR 2018 | [Unsupervised Feature Learning via Non-Parametric Instance Discrimination (NPID, NPID++)](https://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0801.pdf) | NPID: 54.0%, NPID++: 59.0% |
| CVPR 2018 | [Boosting Self-Supervised Learning via Knowledge Transfer (Jigsaw++)](https://openaccess.thecvf.com/content_cvpr_2018/papers/Noroozi_Boosting_Self-Supervised_Learning_CVPR_2018_paper.pdf) | - |
| CVPR 2020 | [Self-Supervised Learning of Pretext-Invariant Representations (PIRL)](https://arxiv.org/pdf/1912.01991.pdf) | 63.6 % |
| CVPR 2020 | [Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics](https://arxiv.org/pdf/2004.02331.pdf) | - |
| arXiv:2003.04298 | [Multi-modal Self-Supervision from Generalized Data Transformations](https://arxiv.org/pdf/2003.04298.pdf) | - |
### Self-supervised learning with Knowledge Distillation
| Conference / Journal | Paper | Method |
| -------------------- | ------------------------------------------------------------ | ------------------------------------------------------- |
| NeurIPS 2020 | [CompRess: Self-Supervised Learning by Compressing Representations](https://arxiv.org/pdf/2010.14713.pdf) | Similarity Distribution + Memory bank |
| ICLR 2021 | [SEED: SELF-SUPERVISED DISTILLATION FOR VISUAL REPRESENTATION](https://arxiv.org/pdf/2101.04731.pdf) | Similarity Distribution + Memory bank |
| arXiv:2104.09124 | [DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning](https://arxiv.org/pdf/2104.09124v3.pdf) | Contrastive Learning w/ Teacher Model |
| arXiv:2104.09866 | [Distill on the Go: Online knowledge distillation in self-supervised learning](https://arxiv.org/pdf/2104.09866.pdf) | Contrastive Learnning w/ Teacher Model |
|arXiv:2104.14294 | [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/pdf/2104.14294.pdf) | Self Distillation w/ Teacher Model|
| ICLR 2022 | [iBOT: Image BERT Pre-Training with Online Tokenizer](https://arxiv.org/pdf/2111.07832.pdf) | Self Distillation w/ Teacher Model + Masked Image Modeling|
| arXiv:2106.11304 | [Simple Distillation Baselines for Improving Small Self-supervised Models](https://arxiv.org/pdf/2106.11304.pdf) | Contrastive Learning w/ Teacher Model + Multi-view loss |
| arXiv:2107.01691 | [Bag of Instances Aggregation Boosts Self-supervised Learning](https://arxiv.org/pdf/2107.01691.pdf) | Bag aggregation |
### Others (in Pretraining / Feature / Representation)
| Conference / Journal | Paper | Method |
| ---------------------- | ------------------------------------------------------------ | ---------------------------- |
| ICLR2018 | [Unsupervised Representation Learning by Predicting Image Rotations](https://arxiv.org/abs/1803.07728) | Surrogate classes, pre-training
| ICML 2018 | [Mutual Information Neural Estimation](https://arxiv.org/pdf/1801.04062.pdf) | Mutual Information |
| NeurIPS 2019 | [Wasserstein Dependency Measure for Representation Learning](http://papers.nips.cc/paper/9692-wasserstein-dependency-measure-for-representation-learning.pdf) | Mutual Information |
| ICLR 2019 | [Learning Deep Representations by Mutual Information Estimation and Maximization](https://arxiv.org/pdf/1808.06670.pdf) | Mutual Information |
| arXiv:1903.12355 | [Local Aggregation for Unsupervised Learning of Visual Embeddings](https://arxiv.org/pdf/1903.12355.pdf) | Local Aggregation |
| arXiv:1906.00910 | [Learning Representations by Maximizing Mutual Information Across Views](https://arxiv.org/pdf/1906.00910.pdf) | Mutual Information |
| arXiv:1907.02544 | [Large Scale Adversarial Representation Learning(BigBiGAN)](https://arxiv.org/pdf/1907.02544.pdf) | Adversarial Training |
| ICLR 2020 | [On Mutual Information Maximization for Representation Learning](https://arxiv.org/pdf/1907.13625.pdf) | Mutual Information |
| CVPR 2020 | [How Useful is Self-Supervised Pretraining for Visual Tasks?](https://arxiv.org/pdf/2003.14323.pdf) | - |
| CVPR 2020 | [Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning](https://arxiv.org/pdf/2003.12862.pdf) | Adversarial Training |
| ICLR 2020 | [Self-Labeling via Simultaneous Clustering and Representation Learning](https://arxiv.org/pdf/1911.05371.pdf) | Information |
| arXiv:1912.11370 | [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/pdf/1912.11370.pdf) | pre-training |
| arXiv:2009.07724 | [Evaluating Self-Supervised Pretraining Without Using Labels](https://arxiv.org/pdf/2009.07724.pdf) | pre-training |
| arXiv:2010.00578 | [UNDERSTANDING SELF-SUPERVISED LEARNING WITH DUAL DEEP NETWORKS](https://arxiv.org/pdf/2010.00578.pdf) | Dual Deep Network |
| ICLR 2021 under review | [REPRESENTATION LEARNING VIA INVARIANT CAUSAL MECHANISMS](https://arxiv.org/pdf/2010.07922.pdf) | Casual mechanism |
| arXiv:2006.06882 | [Rethinking Pre-training and Self-training](https://arxiv.org/pdf/2006.06882.pdf) | Rethinking |
| arXiv:2102.12903 | [Self-Tuning for Data-Efficient Deep Learning](https://arxiv.org/pdf/2102.12903.pdf) | Data-efficient deep learning |
| arXiv:2102.10106 | [Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction](https://arxiv.org/pdf/2102.10106.pdf) | Find similar samples |
| ECCV 2020 | [Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690749.pdf) | Feature embedding & refining |
| arXiv:2102.11150 | [Improving Unsupervised Image Clustering With Robust Learning](https://arxiv.org/pdf/2012.11150.pdf) | Pseudo-label, clustering |
| CVPR 2021 | [How Well Do Self-Supervised Models Transfer?](https://arxiv.org/abs/2011.13377) | Benchmarking |
## Identification / Verification / Classification / Recognition
| Conference / Journal | Paper | Datasets | Performance |
| -------------------- | ------------------------------------------------------------ | ---------- | ------------------- |
| CVPR 2020 | [Real-world Person Re-Identification via Degradation Invariance Learning](https://arxiv.org/pdf/2004.04933.pdf) | MLR-CHUK03 | Acc : 85.7(R@1) |
| CVPR 2020 | [Spatially Attentive Output Layer for Image Classification](https://arxiv.org/pdf/2004.07570.pdf) | ImageNet | Acc : 81.01 (Top-1) |
| CVPR 2020 | [Look-into-Object: Self-supervised Structure Modeling for Object Recognition](https://arxiv.org/pdf/2003.14142.pdf) | ImageNet | Top-1 err : 22.87 |
## Segmentation / Depth Estimation
| Conference / Journal | Paper | Datasets | Performance |
| -------------------- | ------------------------------------------------------------ | --------------- | --------------- |
| CVPR 2020 | [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://arxiv.org/pdf/2004.04581.pdf) | VOC 2012 | mIoU : 64.5 |
| CVPR 2020 | [Towards Better Generalization: Joint Depth-Pose Learning without PoseNet](https://arxiv.org/pdf/2004.01314.pdf) | KITTI 2015 | F1 : 18.05 % |
| IROS 2020 | [Monocular Depth Estimation with Self-supervised Instance Adaptation](https://arxiv.org/pdf/2004.05821.pdf) | KITTI 2015 | Abs Rel : 0.074 |
| CVPR 2020 | [Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera](https://arxiv.org/pdf/2004.01294.pdf) | - | - |
| CVPR 2020 | [Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://arxiv.org/pdf/2004.07703.pdf) | GTA5->Cityscape | mIoU : 46.3 |
| CVPR 2020 | [D3VO : Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://arxiv.org/pdf/2003.01060.pdf) | - | - |
| CVPR 2020 | [Self-Supervised Human Depth Estimation from Monocular Videos](https://arxiv.org/pdf/2005.03358.pdf) | - | - |
| arxiv:2009.07714 | [Calibrating Self-supervised Monocular Depth Estimation](https://arxiv.org/pdf/2009.07714.pdf) | KITTI | Abs Rel: 0.113 |
## Detection / Localization
| Conference / Journal | Paper | Datsets | Performance |
| -------------------- | ------------------------------------------------------------ | -------- | ------------- |
| CVPR 2020 | [Instance-aweare, Context-focused, and Memory-efficient Weakly Supervised Object Detection](https://arxiv.org/pdf/2004.04725.pdf) | VOC 2012 | AP(50) : 67.0 |
## Generation
| Conference / Journal | Paper | Task |
| ---------------------------- | ------------------------------------------------------------ | ------------------------------- |
| CVPR 2020 | [StyleRig: Rigging StyleGAN for 3D Control over Portrait Images](https://arxiv.org/pdf/2004.00121.pdf) | Portrait Images |
| ICLR 2020 | [From Inference to Generation: End-to-End Fully Self-Supervised Generation of Human Face from Speech](https://arxiv.org/pdf/2004.05830.pdf) | Generate human face from speech |
| ACMMM2020 | [Neutral Face Game Character Auto-Creation via PokerFace-GAN](https://arxiv.org/pdf/2008.07154.pdf) | |
| ICLR 2021
under review | [Self-Supervised Variational Auto-Encoders](https://arxiv.org/pdf/2010.02014.pdf) | FID: 34.71 (CIFAR-10) |
## Video
| Conference / Journal | Paper | Task | Performance | Datasets |
| -------------------- | ------------------------------------------------------------ | ----------------------- | ------------------------------ | ---------- |
| TPAMI | [A Review on Deep Learning Techniques for Video Prediction](https://arxiv.org/pdf/2004.05214.pdf) | Video prediction review | - | - |
| CVPR 2020 | [Distilled Semantics for Comprehensive Scene Understanding from Videos](https://arxiv.org/pdf/2003.14030.pdf) | Scene Understanding | Sq Rel : 0.748 | KITTI 2015 |
| CVPR 2020 | [Self-Supervised Learning of Video-Induced Visual Invariances](https://arxiv.org/pdf/1912.02783.pdf) | Representation Learning | - | - |
| ECCV 2020 | [Video Representation Learning by Recognizing Temporal Transformations](https://arxiv.org/pdf/2007.10730.pdf) | Representation Learning | 26.1 % (Video Retrieval Top-1) | UCF101 |
| arXiv:2008.02531 | [Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework](https://arxiv.org/pdf/2008.02531.pdf) | Representation Learning | 42.4 % (Video Retrieval Top-1) | UCF101 |
| NeurIPS 2020 | [Space-Time Correspondence as a Contrastive Random Walk](https://proceedings.neurips.cc/paper/2020/file/e2ef524fbf3d9fe611d5a8e90fefdc9c-Paper.pdf) | Contrastive Learning | 64.8 (Region Similarity) | DAVIS 2017 |
## Others
| Conference / Journal | Paper | Task | Performance |
| --------------------- | ------------------------------------------------------------ | ------------------------------------------------ | ---------------------------------------- |
| CVPR 2020 | [Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching](https://arxiv.org/pdf/2004.02138.pdf) | Optical Flow | F1 : 7.63% (KITTI 2012) |
| CVPR 2020 | [Self-Supervised Viewpoint Learning From Image Collections](https://arxiv.org/pdf/2004.01793.pdf) | Viewpoint learning | MAE : 4.0 (BIWI) |
| CVPR 2020 | [Self-Supervised Scene De-occlusion](https://arxiv.org/pdf/2004.02788.pdf) | Remove occlusion | mAP : 29.3 % (KINS) |
| CVPR 2020 | [Distilled Semantics for Comprehensive Scene Understanding from Videos](https://arxiv.org/pdf/2003.14030.pdf) | Scene Understanding | - |
| CVPR 2020 | [Learning by Analogy : Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://arxiv.org/pdf/2003.13045.pdf) | Optical Flow | F1 : 11.79% (KITTI 2015) |
| CVPR 2020 | [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://arxiv.org/pdf/2003.03164.pdf) | 3D Local Features | - |
| CVPR 2020 | [SpeedNet: Learning the Speediness in Videos](https://arxiv.org/pdf/2004.06130.pdf) | predict the "speediness" | - |
| CVPR 2020 | [Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation](https://arxiv.org/pdf/2003.02824.pdf) | Action Segmentation | F1@10 : 83.0 (GTEA) |
| CVPR 2020 | [MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation](https://arxiv.org/pdf/2003.00667.pdf) | Robotic Navigation | - |
| arXiv:2003.06734 | [Active Perception and Representation for Robotic Manipulation](https://arxiv.org/pdf/2003.06734.pdf) | Robot manipulation | - |
| arXiv:2005.01655 | [Words aren’t enough, their order matters: On the Robustness of Grounding Visual Referring Expressions](https://arxiv.org/pdf/2005.01655.pdf) | Visual Referring Expressions | - |
| arXiv:2004.11362 | [Supervised Contrastive Learning](https://arxiv.org/pdf/2004.11362.pdf) | Supervised Contrastive Learning | ImageNet Acc: 80.8 (Top-1) |
| arXiv:2007.14449 | [Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation](https://arxiv.org/pdf/2007.14449.pdf) | Domain Adaptation | GTA5 to Cityscape : 47.5 (mIoU) |
| arXiv:2007.12360 | [On the Effectiveness of Image Rotation for Open Set Domain Adaptation](https://arxiv.org/pdf/2007.12360.pdf) | Domain Adaptation | - |
| arXiv:2003.12283 | [LIMP: Learning Latent Shape Representations with Metric Preservation Priors](https://arxiv.org/pdf/2003.12283.pdf) | Geneartive models | - |
| arXiv:2004.04312 | [Learning to Scale Multilingual Representations for Vision-Language Tasks](https://arxiv.org/pdf/2004.04312.pdf) | Vision-Language | MSCOCO: 81.5 |
| arXiv:2003.08934 | [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://arxiv.org/pdf/2003.08934.pdf) | View Synthesis | - |
| arXiv:2001.01536 | [Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification](https://arxiv.org/pdf/2001.01536.pdf) | Knowledge Distillation, Long-tail classification | - |
| arXiv:2006.07114 | [Knowledge Distillation Meets Self-Supervision](https://arxiv.org/pdf/2006.07114.pdf) | Knowledge Distillation | Res50 --> MobileNetv2 Acc: 72.57 (Top-1) |
| AAAI2020 | [Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation](https://arxiv.org/pdf/2008.07132.pdf) | Game Character Auto-Creation | - |
| arXiv:2009.07719 | [Domain-invariant Similarity Activation Map Metric Learning for Retrieval-based Long-term Visual Localization](https://arxiv.org/pdf/2009.07719.pdf) | Similarity Activation Map | - |
| arXiv:2008.10312 | [Self-Supervised Learning for Large-Scale Unsupervised Image Clustering](https://arxiv.org/pdf/2008.10312.pdf) | Image Clustering | ImageNet Acc: 38.60 (cluster assignment) |
| ICLR2021 under review | [SSD: A UNIFIED FRAMEWORK FOR SELFSUPERVISED OUTLIER DETECTION](https://openreview.net/pdf?id=v5gjXpmR8J) | Outlier Detection | CIFAR10/CIFAR100 : 94.1% (in/out) |
# Natural Language Processing (NLP)
| Conference / Journal | Paper | Datasets | Performance |
| -------------------- | ------------------------------------------------------------ | -------- | ----------------------- |
| arXiv:2004.03808 | [Improving BERT with Self-Supervised Attention](https://arxiv.org/pdf/2004.03808.pdf) | GLUE | Avg : 79.3 (BERT-SSA-H) |
| arXiv:2004.07159 | [PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation](https://arxiv.org/pdf/2004.07159.pdf) | MARCO | 0.498 (Rouge-L) |
| ACL 2020 | [TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition](https://arxiv.org/pdf/2004.07493.pdf) | - | - |
| arXiv:1909.11942 | [ALBERT: A Lite BERT For Self-Supervised Learning of Language Representations](https://arxiv.org/pdf/1909.11942.pdf) | GLUE | Avg : 89.4 |
| AAAI 2020 | [Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models](https://arxiv.org/pdf/2002.05058.pdf) | - | - |
| ACL 2020 | [Contrastive Self-Supervised Learning for Commonsense Reasoning](https://arxiv.org/pdf/2005.00669.pdf) | PDP-60 | 90.0% |
# Speech
| Conference / Journal | Paper | Datasets | Performance |
| -------------------- | ------------------------------------------------------------ | ----------- | ---------------------- |
| arXiv:1910.05453v3 | [VQ-WAV2VEC: SELF-SUPERVISED LEARNING OF DISCRETE SPEECH REPRESENTATIONS](https://arxiv.org/pdf/1910.05453.pdf) | nov92 | WER : 2.34 |
| arXiv:1911.03912v2 | [EFFECTIVENESS OF SELF-SUPERVISED PRE-TRAINING FOR SPEECH RECOGNITION](https://arxiv.org/pdf/1911.03912.pdf) | Librispeech | WER : 4.0 |
| ICASSP 2020 | [Generative Pre-Training for Speech with Augoregressive Predictive Coding](https://arxiv.org/pdf/1910.12607.pdf) | - | - |
| Interspeech 2020 | [Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition](https://arxiv.org/pdf/2008.06682.pdf) | IEMOCAP | Emotion Acc: 75.458(%) |
## Graph
| Conference / Journal | Paper | Datasets | Performance |
| -------------------- | ------------------------------------------------------------ | ---------------------- | ------------------------ |
| arXiv:2009.05923 | [Contrastive Self-supervised Learning for Graph Classification](https://arxiv.org/pdf/2009.05923.pdf) | PROTEINS | A3-specific:85.80 |
| arXiv:2102.13085 | [Towards Robust Graph Contrastive Learning](https://arxiv.org/pdf/2102.13085.pdf) | Cora, Citeseer, Pubmed | Acc: 82.4 (Cora, GCA-DE) |
| | | | |
## Reinforcement Learning
| Conference / Journal | Paper | Performance |
| -------------------- | ------------------------------------------------------------ | ---------------------------------------------------- |
| arxiv:2009.05923 | [CONTRASTIVE BEHAVIORAL SIMILARITY EMBEDDINGS FOR GENERALIZATION IN REINFORCEMENT LEARNING](https://arxiv.org/pdf/2101.05265.pdf) | BiC-catch: 821±17 (Random Initialization / DrQ+PSEs) |
| | | |
| | | |