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Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning.
On the role of structure in vision
Andrew P Witkin and Jay M Tenenbaum · 1983
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Learning invariance from transformation sequences
Peter Földiák · 1991
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Vision science: Photons to phenomenology
Stephen E Palmer · 1999
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J Sejnowski · 2002
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu Jie Huang · 2006
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Sift flow: Dense correspondence across different scenes
Ce Liu, Jenny Yuen, Antonio Torralba, Josef Sivic, and William T Freeman · 2008
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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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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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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Deformable spatial pyramid matching for fast dense correspondences
Jaechul Kim, Ce Liu, Fei Sha, and Kristen Grauman · 2013
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Deepflow: Large displacement optical flow with deep matching
Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid · 2013
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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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Do convnets learn correspondence?
Jonathan L Long, Ning Zhang, and Trevor Darrell · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, and Alexander C. Berg · 2015
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Generalized deformable spatial pyramid: Geometry-preserving dense correspondence estimation
Junhwa Hur, Hwasup Lim, Changsoo Park, and Sang Chul Ahn · 2015
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Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 2015
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Universal correspondence network
Christopher B Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Chandraker · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Warpnet: Weakly supervised matching for single-view reconstruction
Angjoo Kanazawa, David W Jacobs, and Manmohan Chandraker · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Megdet: A large mini-batch object detector
Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, and Jian Sun · 2018
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Tracking emerges by colorizing videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, and Kevin Murphy · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 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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Learning about an exponential amount of conditional distributions
Mohamed Belghazi, Maxime Oquab, and David Lopez-Paz · 2019
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Mehdi Noroozi and Paolo Favaro · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Cited alongside, same era.
Stereo matching by training a convolutional neural network to compare image patches
Jure Žbontar and Yann LeCun · 2016
Cited alongside, same era.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Learning dense correspondence via 3d-guided cycle consistency
Tinghui Zhou, Philipp Krahenbuhl, Mathieu Aubry, Qixing Huang, and Alexei A Efros · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
Cited alongside, same era.
Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
Cited alongside, same era.
Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
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Video representation learning by dense predictive coding
Tengda Han, Weidi Xie, and Andrew Zisserman · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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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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Scops: Self-supervised co-part segmentation
Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz · 2019
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Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár · 2019
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Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
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Self-supervised learning for video correspondence flow
Z. Lai and W. Xie · 2019
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Joint-task self-supervised learning for temporal correspondence
Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang · 2019
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Unsupervised learning of object structure and dynamics from videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P Murphy, and Honglak Lee · 2019
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Unsupervised learning of landmarks by descriptor vector exchange
James Thewlis, Samuel Albanie, Hakan Bilen, and Andrea Vedaldi · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Learning correspondence from the cycle-consistency of time
Xiaolong Wang, Allan Jabri, and Alexei A Efros · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
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Local aggregation for unsupervised learning of visual embeddings
Chengxu Zhuang, Alex Lin Zhai, and Daniel Yamins · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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