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We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Concrete representation of abstract (m)-spaces (a characterization of the space of continuous functions)
Shizuo Kakutani · 1941
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Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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Annealed importance sampling
Radford M. Neal · 2001
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Envelope theorems for arbitrary choice sets
Paul Milgrom and Ilya Segal · 2002
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Optimal Transport: Old and New
Cédric Villani · 2009
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, and Larry Wasserman · 2014
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Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 2015
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How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
Ferenc Huszar · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Wasserstein training of restricted boltzmann machines
Grégoire Montavon, Klaus-Robert Müller, and Marco Cuturi · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger B. Grosse · 2016
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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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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Stochastic optimization for large-scale optimal transport
Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis Bach · 2016
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Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Generative models and model criticism via optimized maximum mean discrepancy
Dougal J Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2017
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