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Recent works in self-supervised learning have advanced the state-of-the-art by relying on the contrastive learning paradigm, which learns representations by pushing positive pairs, or similar examples from the same class, closer together while keeping negative pairs far apart.
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Improved baselines with momentum contrastive learning
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Mathew Penrose · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
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On clusterings: Good, bad and spectral
Ravi Kannan, Santosh Vempala, and Adrian Vetta · 2004
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Proximity graphs for clustering and manifold learning
Richard Zemel and Miguel Carreira-Perpiñán · 2004
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Co-training and expansion: Towards bridging theory and practice
Maria-Florina Balcan, Avrim Blum, and Ke Yang · 2005
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2005
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Expander flows, geometric embeddings and graph partitioning
Sanjeev Arora, Satish Rao, and Umesh Vazirani · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Semi-supervised learning with the graph laplacian: The limit of infinite unlabelled data
Boaz Nadler, Nathan Srebro, and Xueyuan Zhou · 2009
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Understanding self-supervised learning with dual deep networks
Yuandong Tian, Lantao Yu, Xinlei Chen, and Surya Ganguli · 2010
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Algorithmic extensions of cheeger’s inequality to higher eigenvalues and partitions
Anand Louis, Prasad Raghavendra, Prasad Tetali, and Santosh Vempala · 2011
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Multiway spectral partitioning and higher-order cheeger inequalities
James R Lee, Shayan Oveis Gharan, and Luca Trevisan · 2014
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Approximation algorithm for sparsest k-partitioning
Anand Louis and Konstantin Makarychev · 2014
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For self-supervised learning, rationality implies generalization, provably
Yamini Bansal, Gal Kaplun, and Boaz Barak · 2020
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Phase transitions for detecting latent geometry in random graphs
Matthew Brennan, Guy Bresler, and Dheeraj Nagaraj · 2020
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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
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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
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Ya Le and Xuan Yang · 2015
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Consistency of spectral clustering in stochastic block models
Jing Lei, Alessandro Rinaldo, et al · 2015
Cited alongside, same era.
The geometry of kernelized spectral clustering
Geoffrey Schiebinger, Martin J Wainwright, and Bin Yu · 2015
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Testing for high-dimensional geometry in random graphs
Sébastien Bubeck, Jian Ding, Ronen Eldan, and Miklós Z Rácz · 2016
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Eldan’s stochastic localization and the kls conjecture: Isoperimetry, concentration and mixing
Yin Tat Lee and Santosh S Vempala · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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Community detection and stochastic block models: recent developments, 2017
Emmanuel Abbe · 2017
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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
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Data-efficient image recognition with contrastive predictive coding
Olivier Henaff · 2020
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Predicting what you already know helps: Provable self-supervised learning
Jason D Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Representation learning via invariant causal mechanisms
Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing, and Charles Blundell · 2020
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Contrastive estimation reveals topic posterior information to linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2020
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Self-supervised learning from a multi-view perspective
Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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A theory of label propagation for subpopulation shift
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An almost constant lower bound of the isoperimetric coefficient in the kls conjecture
Yuansi Chen · 2021
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Testing thresholds for high-dimensional sparse random geometric graphs
Siqi Liu, Sidhanth Mohanty, Tselil Schramm, and Elizabeth Yang · 2021
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2021
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Contrastive learning inverts the data generating process
Roland S Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Jeff Z HaoChen, Colin Wei, Ananya Kumar, and Tengyu Ma · 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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