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Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).
Envelope theorems for arbitrary choice sets
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Jorge Nocedal and Stephen Wright · 2006
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Cédric Villani · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
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Martin Arjovsky and Léon Bottou · 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Many paths to equilibrium: Gans do not need to decrease adivergence at every step
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Gans trained by a two time-scale update rule converge to a nash equilibrium
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Jonas Adler and Sebastian Lunz · 2018
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A convex duality framework for gans
Farzan Farnia and David Tse · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2018
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Adashift: Decorrelation and convergence of adaptive learning rate methods
Zhiming Zhou, Qingru Zhang, Guansong Lu, Hongwei Wang, Weinan Zhang, and Yong Yu · 2018
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Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
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On the regularization of wasserstein gans
Henning Petzka, Asja Fischer, and Denis Lukovnicov · 2017
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Large-scale optimal transport and mapping estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, and Mathieu Blondel · 2017
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Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
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Fangyu Zou, Li Shen, Zequn Jie, Weizhong Zhang, and Wei Liu · 2018
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Lipschitz generative adversarial nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang · 2019
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