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Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion.
Sur la distance de deux lois de probabilité
M. Fréchet · 1957
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The Fréchet distance between multivariate normal distributions
D. C. Dowson and B. V. Landau · 1982
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Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Deep generative image models using a Laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 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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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Comparison of maximum likelihood and GAN-based training of real NVPs
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, and Peter Dayan · 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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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier GANs
Augustus Odena, Christopher Olah, and Jonathon Shlens · 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.
Amortised MAP inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2016
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Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
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Youssef Mroueh and Tom Sercu · 2017
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Variational approaches for auto-encoding generative adversarial networks
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, and Shakir Mohamed · 2017
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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
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