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Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue.
Zur theorie der gesellschaftsspiele
J Von Neumann · 1928
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Equilibrium points in n-person games
John F Nash et al · 1950
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 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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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems
Nikos Komodakis and Jean-Christophe Pesquet · 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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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 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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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Dualing gans
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, and Richard Zemel · 2017
Cited alongside, same era.
Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
Cited alongside, same era.
An online learning approach to generative adversarial networks
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause · 2018
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Finding mixed nash equilibria of generative adversarial networks
Ya-Ping Hsieh, Chen Liu, and Volkan Cevher · 2018
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The gan landscape: Losses, architectures, regularization, and normalization
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Beyond local nash equilibria for adversarial networks
Frans A Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol, and Roderich Groß · 2018
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Cited alongside, same era.
Pros and cons of gan evaluation measures
Ali Borji · 2018
Cited alongside, same era.
Xu Chen, Jiang Wang, and Hao Ge · 2018
Cited alongside, same era.
Training gans with optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2018
Cited alongside, same era.
Mevlana Gemici, Zeynep Akata, and Max Welling · 2018
Cited alongside, same era.
Skill rating for generative models
Catherine Olsson, Surya Bhupatiraju, Tom Brown, Augustus Odena, and Ian Goodfellow · 2018
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Fast cosmic web simulations with generative adversarial networks
Andres C. Rodriguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphael Sgier, Janis Fluri, Thomas Hofmann, and Alexandre Réfrégier · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu · 2018
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A variational inequality perspective on generative adversarial nets
Gauthier Gidel, Hugo Berard, Pascal Vincent, and Simon Lacoste-Julien · 2019
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Adversarial generation of time-frequency features with application in audio synthesis
Andrés Marafioti, Nicki Holighaus, Nathanaël Perraudin, and Piotr Majdak · 2019
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