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Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image.
Signature verification using a “siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sackinger, and Roopak Shah · 1994
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Improved baselines with momentum contrastive learning
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, John Winn Christopher K. I. Williams, and Andrew Zisserman · 2010
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Microsoft coco: Common objects in context
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Learning deep features for scene recognition using places database
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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ESC: Dataset for Environmental Sound Classification
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Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Cliquecnn: Deep unsupervised exemplar learning
Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Sutter, and Björn Ommer · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi · 2016
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Joint unsupervised learning of deep representations and image clusters
Jianwei Yang, Devi Parikh, and Dhruv Batra · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Sgdr: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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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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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
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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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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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Learning representations by predicting bags of visual words
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2020
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Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Deep clustering for unsupervised learning
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Vse++: Improving visual-semantic embeddings with hard negatives
Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 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 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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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, Bilal Piot, Koray 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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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Byol works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel Smith, Soham De, Razvan Pascanu, Bilal Piot, and Michal Valko · 2020
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Clusterfit: Improving generalization of visual representations
Xueting Yan, Ishan Misra, Abhinav Gupta, Deepti Ghadiyaram, and Dhruv Mahajan · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Whitening for self-supervised representation learning, 2021
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Online bag-of-visual-words generation for unsupervised representation learning
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven C.H. Hoi · 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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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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