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This work tackles the problem of semi-supervised learning of image classifiers.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 1905
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Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2005
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Semi-Supervised Learning
O. Chapelle, B. Schlkopf, and A. Zien · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
D.-H. Lee · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Semi-supervised learning with deep generative models
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 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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The pascal visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Topology and geometry of half-rectified network optimization
C. D. Freeman and J. Bruna · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Semi-supervised learning with generative adversarial networks
A. Odena · 2016
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Variational autoencoder for deep learning of images, labels and captions
Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, and L. Carin · 2016
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Grasp2Vec: Learning object representations from self-supervised grasping
E. Jang, C. Devin, V. Vanhoucke, and S. Levine · 2018
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Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
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Deep metric transfer for label propagation with limited annotated data
B. Liu, Z. Wu, H. Hu, and S. Lin · 2018
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Boosting self-supervised learning via knowledge transfer
M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, and I. Goodfellow · 2018
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Audio-visual scene analysis with self-supervised multisensory features
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T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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In Defense of the Triplet Loss for Person Re-Identification
A. Hermans, L. Beyer, and B. Leibe · 2017
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii · 2017
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Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 2017
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
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A. Owens and A. A. Efros · 2018
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Cross and learn: Cross-modal self-supervision
N. Sayed, B. Brattoli, and B. Ommer · 2018
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A DIRT-t approach to unsupervised domain adaptation
R. Shu, H. Bui, H. Narui, and S. Ermon · 2018
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There are many consistent explanations of unlabeled data: Why you should average
B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson · 2019
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Mixmatch: A holistic approach to semi-supervised learning
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel · 2019
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Data-efficient image recognition with contrastive predictive coding
O. J. Hénaff, A. Razavi, C. Doersch, S. Eslami, and A. v. d. Oord · 2019
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Revisiting self-supervised visual representation learning
A. Kolesnikov, X. Zhai, and L. Beyer · 2019
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Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
M. Soltanolkotabi, A. Javanmard, and J. D. Lee · 2019
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Interpolation consistency training for semi-supervised learning
V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz · 2019
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Unsupervised data augmentation
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
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