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Self-supervised learning methods are gaining increasing traction in computer vision due to their recent success in reducing the gap with supervised learning.
The period of susceptibility to the physiological effects of unilateral eye closure in kittens
David H Hubel and Torsten N Wiesel · 1970
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Development of the brain depends on the visual environment
Colin Blakemore and Grahame F Cooper · 1970
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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Self-organization in a perceptual network
Ralph Linsker · 1988
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Nonlinear principal component analysis using autoassociative neural networks
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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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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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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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Special issue on perception, action and learning
Gösta H. Granlund · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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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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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Loss functions for image restoration with neural networks
Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 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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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Colorization as a proxy task for visual understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli · 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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
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Piotr Bojanowski and Armand Joulin · 2017
Cited alongside, same era.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
Cited alongside, same era.
Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
The 2017 davis challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, and Luc Van Gool · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Cited alongside, same era.
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
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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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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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Self-supervised relational reasoning for representation learning
Massimiliano Patacchiola and Amos J Storkey · 2020
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi · 2020
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Contrastive learning for unpaired image-to-image translation, 2020
Taesung Park, Alexei A. Efros, Richard Zhang, and Jun-Yan Zhu · 2020
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Space-time correspondence as a contrastive random walk
Allan Jabri, Andrew Owens, and Alexei Efros · 2020
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Training vision transformers for image retrieval
Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, and Hervé Jégou · 2021
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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Mc-ssl0. 0: Towards multi-concept self-supervised learning
Sara Atito, Muhammad Awais, Ammarah Farooq, Zhenhua Feng, and Josef Kittler · 2021
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2021
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Ssast: Self-supervised audio spectrogram transformer
Yuan Gong, Cheng-I Jeff Lai, Yu-An Chung, and James Glass · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong · 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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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Self pre-training with masked autoencoders for medical image analysis
Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, and Prateek Prasanna · 2022
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Masked image modeling advances 3d medical image analysis
Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, Venkat Sethuraman, and Kevin Brown · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Training vision transformers with only 2040 images
Yun-Hao Cao, Hao Yu, and Jianxin Wu · 2022
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