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Generative adversarial networks (GAN) are a powerful subclass of generative models.
How sensitive is the human visual system to the local statistics of natural images?
Holly E Gerhard, Felix A Wichmann, and Matthias Bethge · 2013
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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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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Variational generative stochastic networks with collaborative shaping
Philip Bachman and Doina Precup · 2015
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How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
Ferenc Huszár · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 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
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
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
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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The numerics of GANs
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Conditional image synthesis with auxiliary classifier GANs
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas · 2017
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Do GANs learn the distribution? some theory and empirics
Sanjeev Arora, Andrej Risteski, and Yi Zhang · 2018
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David Berthelot, Tom Schumm, and Luke Metz · 2017
Cited alongside, same era.
Improved training of Wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
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.
On convergence and stability of GANs
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
Cited alongside, same era.
Many paths to equilibrium: GANs do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, and Ian Goodfellow · 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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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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