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In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances.
A theorem for physicists in the theory of random variables
Daniel T Gillespie · 1983
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Optimal transport: old and new , volume 338
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
Earlier work this paper cites.
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Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, and Bernhard Schoelkopf · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
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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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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks
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Image to image translation for domain adaptation
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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Semantic image inpainting with deep generative models
Raymond A Yeh, Chen Chen, Teck Yian Lim, Alexander G Schwing, Mark Hasegawa-Johnson, and Minh N Do · 2017
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