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As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems.
Liii. on lines and planes of closest fit to systems of points in space
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Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables
Irving J Good · 1963
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Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Joseph B Kruskal · 1964
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Some methods for classification and analysis of multivariate observations
J Macqueen · 1967
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Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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On spectral clustering: Analysis and an algorithm
Andrew Ng, Michael Jordan, and Yair Weiss · 2001
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Stochastic neighbor embedding
Geoffrey E Hinton and Sam 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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Scan: Learning to classify images without labels, 2020
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2005
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Debiased contrastive learning, 2020
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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Exploiting compositionality to explore a large space of model structures
Roger Grosse, Ruslan R Salakhutdinov, William T Freeman, and Joshua B Tenenbaum · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Geodesics in heat: A new approach to computing distance based on heat flow
Keenan Crane, Clarisse Weischedel, and Max Wardetzky · 2013
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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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Graph layouts by t-sne
Johannes F Kruiger, Paulo E Rauber, Rafael Messias Martins, Andreas Kerren, Stephen Kobourov, and Alexandru C Telea · 2017
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Emerging properties in self-supervised vision transformers, 2021
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 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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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, 2021
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 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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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, Joao F Henriques, and Andrea Vedaldi · 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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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Contrastive representation learning
Lilian Weng · 2021
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Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods
Randall Balestriero and Yann LeCun · 2022
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Unified contrastive learning in image-text-label space
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Ce Liu, Lu Yuan, and Jianfeng Gao · 2022
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Exploring the limits of deep image clustering using pretrained models
Nikolas Adaloglou, Felix Michels, Hamza Kalisch, and Markus Kollmann · 2023
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Unsupervised visualization of image datasets using contrastive learning
Jan Niklas Böhm, Philipp Berens, and Dmitry Kobak · 2023
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Learning visual representations via language-guided sampling
Mohamed El Banani, Karan Desai, and Justin Johnson · 2023
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Your contrastive learning is secretly doing stochastic neighbor embedding
Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, and Weiran Huang · 2023
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Wenting Li, Jiahong Xue, Xi Zhang, Huacan Chen, Zeyu Chen, Feijuan Huang, and Yuanzhe Cai · 2023
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Harmonic loss trains interpretable ai models
David D Baek, Ziming Liu, Riya Tyagi, and Max Tegmark · 2025
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X-sample contrastive loss: Improving contrastive learning with sample similarity graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Diane Bouchacourt, Pietro Astolfi, Kyunghyun Cho, and Yann LeCun · 2025
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