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GANS are powerful generative models that are able to model the manifold of natural images.
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation
Patrice Y. Simard, Yann A. LeCun, John S. Denker, and Bernard Victorri · 1998
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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The manifold tangent classifier
Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning, 2011
Adam Coates Alessandro Bissacco Bo Wu Andrew Y. Ng Yuval Netzer, Tao Wang · 2011
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Network in network
Min Lin, Qiang Chen, and Shuicheng Yan · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Semi-Supervised Learning with Ladder Networks
A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko · 2015
Cited alongside, same era.
Generative visual manipulation on the natural image manifold
E. Shechtman J.-Y. Zhu, P. Krahenb ¨ uhl and A. A. Efros · 2016
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
A. Radford, L. Metz, and S. Chintala · 2016
Cited alongside, same era.
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
T. Salimans and D. P. Kingma · 2016
Cited alongside, same era.
Improved Techniques for Training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Good semi-supervised learning that requires a bad GAN
Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, and Ruslan Salakhutdinov · 2017
Later among the works it cites.
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
A. Kumar, P. Sattigeri, and P. T. Fletcher · 2017
Later among the works it cites.
Temporal Ensembling for Semi-Supervised Learning
S. Laine and T. Aila · 2017
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Triple generative adversarial nets
Chongxuan Li, Kun Xu, Jun Zhu, and Bo 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
Later among the works it cites.
The Riemannian Geometry of Deep Generative Models
H. Shao, A. Kumar, and P. T. Fletcher · 2017
Later among the works it cites.
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Cited alongside, same era.
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
J. T. Springenberg · 2016
Cited alongside, same era.