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The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations.
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2019 · 1911
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Self-supervised learning of pretext-invariant representations
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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A Simple Framework for Contrastive Learning of Visual Representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020a · 2002
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Improved Baselines with Momentum Contrastive Learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020b · 2003
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M.; Misra, I.; Mairal, J.; Goyal, P.; Bojanowski, P.; and Joulin, A. 2020 · 2006
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P. H.; Buchatskaya, E.; Doersch, C.; Pires, B. A.; Guo, Z. D.; Azar, M. G.; et al. 2020 · 2006
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Dimensionality reduction by learning an invariant mapping
Hadsell, R.; Chopra, S.; and LeCun, Y. 2006 · 2006
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Whitening for self-supervised representation learning
Ermolov, A.; Siarohin, A.; Sangineto, E.; and Sebe, N. 2020b · 2007
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Extracting and composing robust features with denoising autoencoders
Vincent, P.; Larochelle, H.; Bengio, Y.; and Manzagol, P.-A. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; et al. 2009 · 2009
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Krothapalli, U.; and Abbott, A. L. 2020 · 2009
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The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
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Hard negative mixing for contrastive learning
Kalantidis, Y.; Sariyildiz, M. B.; Pion, N.; Weinzaepfel, P.; and Larlus, D. 2020 · 2010
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MixCo: Mix-up Contrastive Learning for Visual Representation
Kim, S.; Lee, G.; Bae, S.; and Yun, S.-Y. 2020 · 2010
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i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Lee, K.; Zhu, Y.; Sohn, K.; Li, C.-L.; Shin, J.; and Lee, H. 2021 · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; and Manzagol, P.-A. 2010 · 2010
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Exploring Simple Siamese Representation Learning
Chen, X.; and He, K. 2020 · 2011
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An analysis of single-layer networks in unsupervised feature learning
Coates, A.; Ng, A.; and Lee, H. 2011 · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J.; Meier, U.; Cireşan, D.; and Schmidhuber, J. 2011 · 2011
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Zhang, R.; Isola, P.; and Efros, A. A. 2017 · 2017
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Deep clustering for unsupervised learning of visual features
Caron, M.; Bojanowski, P.; Joulin, A.; and Douze, M. 2018 · 2018
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Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S.; Singh, P.; and Komodakis, N. 2018 · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D.; Fedorov, A.; Lavoie-Marchildon, S.; Grewal, K.; Bachman, P.; Trischler, A.; and Bengio, Y. 2018 · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Towards better decoding and language model integration in sequence to sequence models
Chorowski, J.; and Jaitly, N. 2016 · 2016
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Donahue, J.; Krähenbühl, P.; and Darrell, T. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M.; and Favaro, P. 2016 · 2016
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z.; Xiong, Y.; Yu, S. X.; and Lin, D. 2018 · 2018
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mixup: Beyond Empirical Risk Minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
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Learning representations by maximizing mutual information across views
Bachman, P.; Hjelm, R. D.; and Buchwalter, W. 2019 · 2019
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Large scale adversarial representation learning
Donahue, J.; and Simonyan, K. 2019 · 2019
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When does label smoothing help?
Müller, R.; Kornblith, S.; and Hinton, G. E. 2019 · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
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Manifold mixup: Better representations by interpolating hidden states
Verma, V.; Lamb, A.; Beckham, C.; Najafi, A.; Mitliagkas, I.; Lopez-Paz, D.; and Bengio, Y. 2019 · 2019
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Detectron2
Wu, Y.; Kirillov, A.; Massa, F.; Lo, W.-Y.; and Girshick, R. 2019 · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Ye, M.; Zhang, X.; Yuen, P. C.; and Chang, S.-F. 2019 · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019
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Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
Shen, Z.; Liu, Z.; Xu, D.; Chen, Z.; Cheng, K.-T.; and Savvides, M. 2021 · 2021
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