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Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart.
The approximation of one matrix by another of lower rank
Carl Eckart and G. Marion Young · 1936
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Choosing a point from the surface of a sphere
George Marsaglia · 1972
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A note on the use of principal components in regression
Ian T Jolliffe · 1982
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Modular learning in neural networks
Dana H Ballard · 1987
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Auto-association by multilayer perceptrons and singular value decomposition
Hervé Bourlard and Yves Kamp · 1988
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Matrix computations (3rd ed.)
Gene H. Golub and Charles Van Loan · 1996
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On the distribution of the largest eigenvalue in principal components analysis
Iain M Johnstone · 2001
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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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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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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
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Independent component analysis
Aapo Hyvärinen, Jarmo Hurri, Patrik O Hoyer, Aapo Hyvärinen, Jarmo Hurri, and Patrik O Hoyer · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds
Elnaz Barshan, Ali Ghodsi, Zohreh Azimifar, and Mansoor Zolghadri Jahromi · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, A. Ng, and Honglak Lee · 2011
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On sample eigenvalues in a generalized spiked population model
Zhidong Bai and Jianfeng Yao · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The logistic-normal integral and its generalizations
Dan Pirjol · 2013
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Information-theoretically optimal sparse pca
Yash Deshpande and Andrea Montanari · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Sample covariance matrices and high-dimensional data analysis
Jianfeng Yao, Shurong Zheng, and ZD Bai · 2015
Cited alongside, same era.
A useful variant of the davis–kahan theorem for statisticians
Yi Yu, Tengyao Wang, and Richard J Samworth · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
T Tony Cai and Anru Zhang · 2018
Cited alongside, same era.
Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 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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Prediction under latent factor regression: Adaptive pcr, interpolating predictors and beyond
Xin Bing, Florentina Bunea, Seth Strimas-Mackey, and Marten Wegkamp · 2021
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Dissecting supervised constrastive learning
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Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and E Weinan · 2018
Cited alongside, same era.
Triplet-center loss for multi-view 3d object retrieval
Xinwei He, Yang Zhou, Zhichao Zhou, Song Bai, and Xiang Bai · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
From principal subspaces to principal components with linear autoencoders
Elad Plaut · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
Heteroskedastic pca: Algorithm, optimality, and applications
Anru R Zhang, T Tony Cai, and Yihong Wu · 2018
Cited alongside, same era.
Florian Graf, Christoph Hofer, Marc Niethammer, and Roland Kwitt · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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A broad study on the transferability of visual representations with contrastive learning
Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Richard J. Radke, and Rogério Schmidt Feris · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Predicting what you already know helps: Provable self-supervised learning
Jason D Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2021
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Self-supervised learning with kernel dependence maximization
Yazhe Li, Roman Pogodin, Danica J Sutherland, and Arthur Gretton · 2021
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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 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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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael Jordan · 2021
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Towards demystifying representation learning with non-contrastive self-supervision
Xiang Wang, Xinlei Chen, Simon S Du, and Yuandong Tian · 2021
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Toward understanding the feature learning process of self-supervised contrastive learning
Zixin Wen and Yuanzhi Li · 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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The details matter: Preventing class collapse in supervised contrastive learning
Daniel Y Fu, Mayee F Chen, Michael Zhang, Kayvon Fatahalian, and Christopher Ré · 2022
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The dynamics of representation learning in shallow, non-linear autoencoders
Maria Refinetti and Sebastian Goldt · 2022
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Understanding contrastive learning requires incorporating inductive biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, and Akshay Krishnamurthy · 2022
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Understanding deep contrastive learning via coordinate-wise optimization
Yuandong Tian · 2022
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