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We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design.
Quasi-equilibria in markets with non-convex preferences
Ross M Starr · 1969
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Min common/max crossing duality: A geometric view of conjugacy in convex optimization
D Bertsekas · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Efficient learning of deep Boltzmann machines
Ruslan Salakhutdinov and Hugo Larochelle · 2010
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 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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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Generative multi-adversarial networks
Ishan Durugkar, Ian Gemp, and Sridhar Mahadevan · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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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.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Martin 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.
BEGAN: boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
Cited alongside, same era.
MAGAN: Margin adaptation for generative adversarial networks
Ruohan Wang, Antoine Cully, Hyung Jin Chang, and Yiannis Demiris · 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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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Matrix completion and related problems via strong duality
Maria-Florina Balcan, Yingyu Liang, David P Woodruff, and Hongyang Zhang · 2018
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MGAN: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
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Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
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Multi-agent diverse generative adversarial networks
Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip HS Torr, and Puneet K Dokania · 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.
Dual discriminator generative adversarial nets
Tu Nguyen, Trung Le, Hung Vu, and Dinh Phung · 2017
Cited alongside, same era.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Stackelberg security games: Looking beyond a decade of success
Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, and Milind Tambe · 2018
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song · 2018
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Deep neural networks with multi-branch architectures are less non-convex
Hongyang Zhang, Junru Shao, and Ruslan Salakhutdinov · 2018
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