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Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget.
Classes of kernels for machine learning: A statistics perspective
Marc G. Genton · 2002
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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On surrogate loss functions and f-divergences
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Accelerating t-SNE using tree-based algorithms
Laurens van der Maaten · 2014
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Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 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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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 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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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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A guide to convolution arithmetic for deep learning, 2016
Vincent Dumoulin and Francesco Visin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Amortised map inference for image super-resolution
Casper K. Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2017
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Hierarchical implicit models and likelihood-free variational inference, 2017
Dustin Tran, Rajesh Ranganath, and David M. Blei · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan. Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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On gradient regularizers for MMD GANs
Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, and Arthur Gretton · 2018
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Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Class-splitting generative adversarial networks, 2017
Guillermo L. Grinblat, Lucas C. Uzal, and Pablo M. Granitto · 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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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.
Stacked generative adversarial networks
Xun Huang, Yixuan Li, Omid Poursaeed, John E. Hopcroft, and Serge J. Belongie · 2017
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Semi-supervised learning for optical flow with generative adversarial networks
Wei-Sheng Lai, Jia-Bin Huang, and Ming-Hsuan Yang · 2017
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Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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cGANs with projection discriminator
Takeru Miyato and Masanori Koyama · 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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Lipschitz-margin training: scalable certification of perturbation invariance for deep neural networks
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
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Coulomb GANs: Provably optimal Nash equilibria via potential fields
Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, and Sepp Hochreiter · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
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Self-attention generative adversarial networks, 2018
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2018
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Activation maximization generative adversarial nets
Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, and Yong Yu · 2018
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The singular values of convolutional layers
Hanie Sedghi, Vineet Gupta, and Philip M. Long · 2019
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