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In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.
Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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Cat head detection-how to effectively exploit shape and texture features
Weiwei Zhang, Jian Sun, and Xiaoou Tang · 2008
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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 P Kingma and Jimmy Ba · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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, Xi Chen, and Xi Chen · 2016
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How generative adversarial nets and its variants work: An overview of gan
Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, and Sungroh Yoon · 2017
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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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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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
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Many paths to equilibrium: Gans do not need to decrease adivergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2017
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Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Gans trained by a two time-scale update rule converge to a nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter · 2017
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Pacgan: The power of two samples in generative adversarial networks
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2017
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Fisher gan
Youssef Mroueh and Tom Sercu · 2017
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Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2017
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Gans beyond divergence minimization
Alexia Jolicoeur-Martineau · 2018
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Pros and cons of gan evaluation measures
Ali Borji · 2018
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