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We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN).
Integral probability metrics and their generating classes of functions
Muller, Alfred · 1997
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Log-euclidean metrics for fast and simple calculus on diffusion tensors
Arsigny, Vincent, Fillard, Pierre, Pennec, Xavier, and Ayache, Nicholas · 2006
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Optimization Algorithms on Matrix Manifolds
Absil, P.-A., Mahony, R., and Sepulchre, R · 2007
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Huang, Gary B., Ramesh, Manu, Berg, Tamara, and Learned-Miller, Erik · 2007
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Random features for large-scale kernel machines
Rahimi, Ali and Recht, Benjamin · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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On integral probability metrics, phi -divergences and binary classification, 2009
Sriperumbudur, Bharath K., Fukumizu, Kenji, Gretton, Arthur, Schölkopf, Bernhard, and Lanckriet, Gert R. G · 2009
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A kernel two-sample test
Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte J., Schölkopf, Bernhard, and Smola, Alexander · 2012
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On the empirical estimation of integral probability metrics
Sriperumbudur, Bharath K., Fukumizu, Kenji, Gretton, Arthur, Schölkopf, Bernhard, and Lanckriet, Gert R. G · 2012
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Auto-encoding variational bayes
Kingma, Diederik P. and Welling, Max · 2013
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Conditional generative adversarial nets
Mirza, Mehdi and Osindero, Simon · 2014
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Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, Gintare Karolina, Roy, Daniel M., and Ghahramani, Zoubin · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Generative moment matching networks
Li, Yujia, Swersky, Kevin, and Zemel, Richard · 2015
Kernel mean embedding of distributions: A review and beyond
Muandet, Krikamol, Fukumizu, Kenji, Sriperumbudur, Bharath, and Schölkopf, Bernhard · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, Sebastian, Cseke, Botond, and Tomioka, Ryota · 2016
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Conditional image synthesis with auxiliary classifier gans
Odena, Augustus, Olah, Christopher, and Shlens, Jonathon · 2016
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Improved techniques for training gans
Salimans, Tim, Goodfellow, Ian, Zaremba, Wojciech, Cheung, Vicki, Radford, Alec, Chen, Xi, and Chen, Xi · 2016
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A note on the evaluation of generative models
Theis, Lucas, Oord, Aäron van den, and Bethge, Matthias · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, Fisher, Zhang, Yinda, Song, Shuran, Seff, Ari, and Xiao, Jianxiong · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, Xi, Duan, Yan, Houthooft, Rein, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter · 2016
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Wasserstein gan
Arjovsky, Martin, Chintala, Soumith, and Bottou, Leon · 2017
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Began: Boundary equilibrium generative adversarial networks
Berthelot, David, Schumm, Tom, and Metz, Luke · 2017
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Image-to-image translation with conditional adversarial networks
Isola, Phillip, Zhu, Jun-Yan, Zhou, Tinghui, and Efros, Alexei A · 2017
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Energy based generative adversarial networks
Zhao, Junbo, Mathieu, Michael, and Lecun, Yann · 2017
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