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Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches.
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
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Dimensionality reduction by learning an invariant mapping
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Automatic data augmentation for generalization in deep reinforcement learning
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Distance metric learning for large margin nearest neighbor classification
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P. Vincent, H. Larochelle, Y. Bengio, and P.-A. 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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Learning multiple layers of features from tiny images
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SSMBA: self-supervised manifold based data augmentation for improving out-of-domain robustness
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Understanding self-supervised learning with dual deep networks
Y. Tian, L. Yu, X. Chen, and S. Ganguli · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
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Exploring simple siamese representation learning
X. Chen and K. He · 2011
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An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
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Online bag-of-visual-words generation for unsupervised representation learning
S. Gidaris, A. Bursuc, G. Puy, N. Komodakis, M. Cord, and P. Pérez · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
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Auto-encoding variational bayes
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
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Avoiding latent variable collapse with generative skip models
A. B. Dieng, Y. Kim, A. M. Rush, and D. M. Blei · 2019
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Pytorch lightning
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Scaling and benchmarking self-supervised visual representation learning
P. Goyal, D. Mahajan, A. Gupta, and I. Misra · 2019
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Data-efficient image recognition with contrastive predictive coding
O. J. Hénaff, A. Srinivas, J. D. Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. van den Oord · 2019
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Detectron2
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick · 2019
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I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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f-gan: Training generative neural samplers using variational divergence minimization, 2016
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Multi-task self-supervised visual learning
C. Doersch and A. Zisserman · 2017
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Accurate, large minibatch SGD: training imagenet in 1 hour
P. Goyal, P. Dollár, R. B. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton · 2020
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Debiased contrastive learning
C. Chuang, J. Robinson, Y. Lin, A. Torralba, and S. Jegelka · 2020
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A framework for contrastive self-supervised learning and designing a new approach
W. Falcon and K. Cho · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. B. Girshick · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. van der Maaten · 2020
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Byol works even without batch statistics, 2020
P. H. Richemond, J.-B. Grill, F. Altché, C. Tallec, F. Strub, A. Brock, S. Smith, S. De, R. Pascanu, B. Piot, and M. Valko · 2020
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What makes for good views for contrastive learning?
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
A. Bardes, J. Ponce, and Y. LeCun · 2021
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Self-supervised pretraining of visual features in the wild
P. Goyal, M. Caron, B. Lefaudeux, M. Xu, P. Wang, V. Pai, M. Singh, V. Liptchinsky, I. Misra, A. Joulin, and P. Bojanowski · 2021
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Contrastive learning with hard negative samples
J. D. Robinson, C. Chuang, S. Sra, and S. Jegelka · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny · 2021
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