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Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Signature verification using a” siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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A random walks view of spectral segmentation
Marina Meilă and Jianbo Shi · 2001
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce · 2006
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Segmentation of multivariate mixed data via lossy data coding and compression
Yi Ma, Harm Derksen, Wei Hong, and John Wright · 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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Nonlinear learning using local coordinate coding
Kai Yu, Tong Zhang, and Yihong Gong · 2009
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Improving the fisher kernel for large-scale image classification
Florent Perronnin, Jorge Sánchez, and Thomas Mensink · 2010
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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The geometry of kernelized spectral clustering
Geoffrey Schiebinger, Martin J Wainwright, and Bin Yu · 2015
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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 learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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The sparse manifold transform
Yubei Chen, Dylan Paiton, and Bruno Olshausen · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Wieland Brendel and Matthias Bethge · 2019
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Towards good practices in self-supervised representation learning
Srikar Appalaraju, Yi Zhu, Yusheng Xie, and István Fehérvári · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Training gans with stronger augmentations via contrastive discriminator
Jongheon Jeong and Jinwoo Shin · 2021
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Efficient self-supervised vision transformers for representation learning
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, and Jianfeng Gao · 2021
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The unreasonable effectiveness of patches in deep convolutional kernels methods
Louis Thiry, Michael Arbel, Eugene Belilovsky, and Edouard Oyallon · 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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Ressl: Relational self-supervised learning with weak augmentation
Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu · 2021
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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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 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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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, et al · 2020
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Scan: Learning to classify images without labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Intra-instance VICReg: Bag of self-supervised image patch embedding
Yubei Chen, Adrien Bardes, Zengyi Li, and Yann LeCun · 2022
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Minimalistic unsupervised learning with the sparse manifold transform
Yubei Chen, Zeyu Yun, Yi Ma, Bruno Olshausen, and Yann LeCun · 2022
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solo-learn: A library of self-supervised methods for visual representation learning
Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci · 2022
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Ctrl: Closed-loop transcription to an ldr via minimaxing rate reduction
Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung-Yeung Shum, et al · 2022
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Self-supervised models are continual learners
Enrico Fini, Victor G Turrisi Da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, and Julien Mairal · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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A path towards autonomous machine intelligence
Yann LeCun · 2022
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Mage: Masked generative encoder to unify representation learning and image synthesis
Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, and Dilip Krishnan · 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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Unsupervised learning of structured representations via closed-loop transcription
Shengbang Tong, Xili Dai, Yubei Chen, Mingyang Li, Zengyi Li, Brent Yi, Yann LeCun, and Yi Ma · 2022
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Incremental learning of structured memory via closed-loop transcription
Shengbang Tong, Xili Dai, Ziyang Wu, Mingyang Li, Brent Yi, and Yi Ma · 2022
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Asher Trockman and J Zico Kolter · 2022
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Unsupervised manifold linearizing and clustering
Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, and Benjamin D Haeffele · 2023
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