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Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data.
On the origin of number and arrangement of the places of exit on the surface of pollen-grains
Pieter Merkus Lambertus Tammes · 1930
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Equilibrium configurations of n equal charges on a sphere
T Erber and GM Hockney · 1991
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How different can colours be? maximum separation of points on a spherical octant
JBM Melisseny · 1998
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Stochastic neighbor embedding
Geoffrey Hinton and Sam T Roweis · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Learning a parametric embedding by preserving local structure
Laurens Van Der Maaten · 2009
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Gromov–wasserstein distances and the metric approach to object matching
Facundo Mémoli · 2011
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N. Rockmore · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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An analysis of the t-sne algorithm for data visualization
Sanjeev Arora, Wei Hu, and Pravesh K Kothari · 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 · 2018
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Stochastic neighbor embedding under f-divergences
Daniel Jiwoong Im, Nakul Verma, and Kristin Branson · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Learning generative models across incomparable spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, and Stefanie Jegelka · 2019
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Clustering with t-sne, provably
George C Linderman and Stefan Steinerberger · 2019
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Doubly stochastic neighbor embedding on spheres
Yao Lu, Jukka Corander, and Zhirong Yang · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 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
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Cert: Contrastive self-supervised learning for language understanding
Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, and Pengtao Xie · 2020
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Declutr: Deep contrastive learning for unsupervised textual representations
John M Giorgi, Osvald Nitski, Gary D Bader, and Bo Wang · 2020
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The power of contrast for feature learning: A theoretical analysis
Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, and Linjun Zhang · 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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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Contrastive clustering
Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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, et al · 2020
Cited alongside, same era.
A trainable optimal transport embedding for feature aggregation
Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, and Julien Mairal · 2020
Cited alongside, same era.
Implicit bias in deep linear classification: Initialization scale vs training accuracy
Edward Moroshko, Blake E Woodworth, Suriya Gunasekar, Jason D Lee, Nati Srebro, and Daniel Soudry · 2020
Cited alongside, same era.
Implicit regularization in deep learning may not be explainable by norms
Noam Razin and Nadav Cohen · 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 learning, multi-view redundancy, and 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
Tongzhou Wang and Phillip Isola · 2020
Cited alongside, same era.
Gromov-wasserstein distances between gaussian distributions
Antoine Salmona, Julie Delon, and Agnès Desolneux · 2021
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Towards domain-agnostic contrastive learning
Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, and Quoc Le · 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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Consert: A contrastive framework for self-supervised sentence representation transfer
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu · 2021
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Instance localization for self-supervised detection pretraining
Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin · 2021
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Filip: Fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu · 2021
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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
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 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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Randall Balestriero and Yann LeCun · 2022
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Unsupervised visualization of image datasets using contrastive learning
Jan Niklas Böhm, Philipp Berens, and Dmitry Kobak · 2022
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From t t -sne to umap with contrastive learning
Sebastian Damrich, Niklas Böhm, Fred A Hamprecht, and Dmitry Kobak · 2022
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Zood: Exploiting model zoo for out-of-distribution generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Tianyang Hu, Yongxin Yang, Sung-Ho Bae, and Zhenguo Li · 2022
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Jeff Z HaoChen, Colin Wei, Ananya Kumar, and Tengyu Ma · 2022
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Neural manifold clustering and embedding
Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer · 2022
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Task-customized self-supervised pre-training with scalable dynamic routing
Zhili Liu, Jianhua Han, Kai Chen, Lanqing Hong, Hang Xu, Chunjing Xu, and Zhenguo Li · 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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Augmentation-free graph contrastive learning
Haonan Wang, Jieyu Zhang, Qi Zhu, and Wei Huang · 2022
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Deciphering the projection head: Representation evaluation self-supervised learning
Jiajun Ma, Tianyang Hu, and Wenjia Wang · 2023
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Arcl: Enhancing contrastive learning with augmentation-robust representations
Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, and Weiran Huang · 2023
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