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Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Unsupervised representation learning by sorting sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
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The iNaturalist Species Classification and Detection Dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Critical learning periods in deep neural networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2019
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Yin Cui, Zeqi Gu, Dhruv Mahajan, Laurens Van Der Maaten, Serge Belongie, and Ser-Nam Lim · 2019
Earlier work this paper cites.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
Earlier work this paper cites.
Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
Cited alongside, same era.
Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
Cited alongside, same era.
Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos · 2020
Cited alongside, same era.
Parametric instance classification for unsupervised visual feature learning
Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, and Han Hu · 2020
Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2020
Later among the works it cites.
Geography-aware self-supervised learning
Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon · 2021
Closest in time.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Intriguing properties of contrastive losses
Ting Chen, Calvin Luo, and Lala Li · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Cited alongside, same era.
Elijah Cole, Benjamin Deneu, Titouan Lorieul, Maximilien Servajean, Christophe Botella, Dan Morris, Nebojsa Jojic, Pierre Bonnet, and Alexis Joly · 2020
Cited alongside, same era.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Closest in time.
How well do self-supervised models transfer?
Linus Ericsson, Henry Gouk, and Timothy M Hospedales · 2021
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Self-supervised pretraining of visual features in the wild
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, et al · 2021
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Efficient visual pretraining with contrastive detection
Olivier J Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, and João Carreira · 2021
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Contrasting contrastive self-supervised representation learning pipelines
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, and Roozbeh Mottaghi · 2021
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Contrastive learning of general-purpose audio representations
Aaqib Saeed, David Grangier, and Neil Zeghidour · 2021
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Concept generalization in visual representation learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus, and Karteek Alahari · 2021
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Moco pretraining improves representation and transferability of chest x-ray models
Hari Sowrirajan, Jingbo Yang, Andrew Y Ng, and Pranav Rajpurkar · 2021
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Data valuation for medical imaging using shapley value and application to a large-scale chest x-ray dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A Dunnmon, James Zou, and Daniel L Rubin · 2021
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Divide and contrast: Self-supervised learning from uncurated data
Yonglong Tian, Olivier J Henaff, and Aaron van den Oord · 2021
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Revisiting contrastive methods for unsupervised learning of visual representations
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
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Benchmarking representation learning for natural world image collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha · 2021
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Exploring cross-image pixel contrast for semantic segmentation
Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, and Luc Van Gool · 2021
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Resnet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
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Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross Girshick · 2021
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What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson WH Lau, and Stephen Lin · 2021
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Vision models are more robust and fair when pretrained on uncurated images without supervision
Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Mannat Singh, Ishan Misra, Levent Sagun, Armand Joulin, and Piotr Bojanowski · 2022
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