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Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches.
Signature verification using a “siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sackinger, and Roopak Shah · 1994
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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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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Ica with reconstruction cost for efficient overcomplete feature learning
Quoc Le, Alexandre Karpenko, Jiquan Ngiam, and Andrew Ng · 2011
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Concise formulas for the area and volume of a hyperspherical cap
Shengqiao Li · 2011
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Deep clustering for unsupervised learning
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Earlier work this paper cites.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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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, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 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.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
Cited alongside, same era.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jegou, and Julien Mairal Piotr Bojanowski Armand Joulin · 2021
Cited alongside, same era.
Decoupled contrastive learning
Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, and Yann LeCun · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Later among the works it cites.
Randall Balestriero and Yann LeCun · 2022
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Watermarking images in self-supervised latent spaces
Pierre Fernandez, Alexandre Sablayrolles, Teddy Furon, Hervé Jégou, and Matthijs Douze · 2022
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Beyond supervised vs. unsupervised: Representative benchmarking and analysis of image representation learning
Matthew Gwilliam and Abhinav Shrivastava · 2022
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Whitening for self-supervised representation learning, 2021
Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, and Nicu Sebe · 2021
Cited alongside, same era.
Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
Cited alongside, same era.
Towards the generalization of contrastive self-supervised learning
Weiran Huang, Mingyang Yi, and Xuyang Zhao · 2021
Cited alongside, same era.
Exploring the equivalence of siamese self-supervised learning via a unified gradient framework
Chenxin Tao, Honghui Wang, Xizhou Zhu, Jiahua Dong, Shiji Song, Gao Huang, and Jifeng Dai · 2021
Cited alongside, same era.
Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 2021
Cited alongside, same era.
A note on connecting barlow twins with negative-sample-free contrastive learning
Yao-Hung Hubert Tsai, Shaojie Bai, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2021
Cited alongside, same era.
Toward understanding the feature learning process of self-supervised contrastive learning
Zixin Wen and Yuanzhi Li · 2021
Cited alongside, same era.
Predictor networks and stop-grads provide implicit variance regularization in byol/simsiam
Manu Srinath Halvagal, Axel Laborieux, and Friedemann Zenke · 2022
Closest in time.
Jeff Z HaoChen, Colin Wei, Ananya Kumar, and Tengyu Ma · 2022
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2022
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Contrasting the landscape of contrastive and non-contrastive learning
Ashwini Pokle, Jinjin Tian, Yuchen Li, and Andrej Risteski · 2022
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Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation
Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z HaoChen, Tengyu Ma, and Percy Liang · 2022
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Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic · 2022
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Chaoning Zhang, Kang Zhang, Trung X Pham, Axi Niu, Zhinan Qiao, Chang D Yoo, and In So Kweon · 2022
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