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Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning.
Scaling universalities of kth-nearest neighbor distances on closed manifolds
Allon G Percus and Olivier C Martin · 1998
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A Strong Law for the Longest Edge of the Minimal Spanning Tree
Mathew D. Penrose · 1999
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Further reverse results for jensen’s discrete inequality and applications in information theory
Ivan Budimir, Sever S Dragomir, and Josep Pecaric · 2000
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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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Scale-invariant convolutional neural networks
Yichong Xu, Tianjun Xiao, Jiaxing Zhang, Kuiyuan Yang, and Zheng Zhang · 2014
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RIFD-CNN: Rotation-invariant and fisher discriminative convolutional neural networks for object detection
Gong Cheng, Peicheng Zhou, and Junwei Han · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 2019
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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
Jean-Bastien Grill, Florian Strub, Florent Altché, C. Tallec, Pierre H. Richemond, Elena Buchatskaya, C. Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, B. Piot, K. Kavukcuoglu, Rémi Munos, and Michal Valko · 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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Predicting what you already know helps: Provable self-supervised learning
Jason D Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2020
Investigating the role of negatives in contrastive representation learning
Jordan T Ash, Surbhi Goel, Akshay Krishnamurthy, and Dipendra Misra · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 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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Towards the generalization of contrastive self-supervised learning
Weiran Huang, Mingyang Yi, and Xuyang Zhao · 2021
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Understanding negative samples in instance discriminative self-supervised representation learning
Kento Nozawa and Issei Sato · 2021
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Cited alongside, same era.
About contrastive unsupervised representation learning for classification and its convergence
Ibrahim Merad, Yiyang Yu, Emmanuel Bacry, and Stéphane Gaïffas · 2020
Cited alongside, same era.
Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 2020
Cited alongside, same era.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Cited alongside, same era.
Contrastive estimation reveals topic posterior information to linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2020
Cited alongside, same era.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere, 2020
Tongzhou Wang and Phillip Isola · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton
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Selfaugment: Automatic augmentation policies for self-supervised learning
Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, and Kurt Keutzer · 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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Self-supervised learning from a multi-view perspective
Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2021
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Residual relaxation for multi-view representation learning
Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, and Zhouchen Lin · 2021
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