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Compared with standard supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data.
Transformation invariance in pattern recognition—tangent distance and tangent propagation
P. Y. Simard, Y. A. LeCun, J. S. Denker, and B. Victorri · 1998
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Algorithms for manifold learning
L. Cayton · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
M. Belkin, P. Niyogi, and V. Sindhwani · 2006
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Explaining brightness illusions using spatial filtering and local response normalization
A. E. Robinson, P. S. Hammon, and V. R. de Sa · 2007
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
O. Chapelle, B. Scholkopf, and A. Zien · 2009
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Sample complexity of testing the manifold hypothesis
H. Narayanan and S. Mitter · 2010
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The manifold tangent classifier
S. Rifai, Y. N. Dauphin, P. Vincent, Y. Bengio, and X. Muller · 2011
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Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G. Alain, and P. Vincent · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Manifold regularization and semi-supervised learning: Some theoretical analyses
P. Niyogi · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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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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Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2016
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Semi-supervised learning with gans: Manifold invariance with improved inference
A. Kumar, P. Sattigeri, and T. Fletcher · 2017
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Triple generative adversarial nets
C. Li, K. Xu, J. Zhu, and B. Zhang · 2017
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Smooth neighbors on teacher graphs for semi-supervised learning
Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang · 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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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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S. Laine and T. Aila · 2016
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Adversarial training methods for semi-supervised text classification
T. Miyato, A. M. Dai, and I. Goodfellow · 2016
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Semi-supervised learning with generative adversarial networks
A. Odena · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Good semi-supervised learning that requires a bad gan
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov · 2017
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Semi-supervised learning via compact latent space clustering
K. Kamnitsas, D. C. Castro, L. L. Folgoc, I. Walker, R. Tanno, D. Rueckert, B. Glocker, A. Criminisi, and A. Nori · 2018
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Semi-supervised learning with gans: Revisiting manifold regularization
B. Lecouat, C.-S. Foo, H. Zenati, and V. R. Chandrasekhar · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow · 2018
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Global versus localized generative adversarial nets
G.-J. Qi, L. Zhang, H. Hu, M. Edraki, J. Wang, and X.-S. Hua · 2018
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