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We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised learning.
Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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On integral probability metrics, phi-divergences and binary classification
Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert RG Lanckriet · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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On the empirical estimation of integral probability metrics
Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert RG Lanckriet, et al · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Normalizing the normalizers: Comparing and extending network normalization schemes
Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H Sinz, and Richard S Zemel · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Wasserstein gan
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Mcgan: Mean and covariance feature matching gan
Youssef Mroueh, Tom Sercu, and Vaibhava Goel · 2017
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Youssef Mroueh and Tom Sercu · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan Salakhutdinov · 2017
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Amortised map inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, and Zhen Wang · 2017
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Sobolev gan
Anonymous Authors · 2017
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2017
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Improved semi-supervised learning with gans using manifold invariances
Abhishek Kumar, Prasanna Sattigeri, and P Thomas Fletcher · 2017
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2017
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