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In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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A comprehensive foundation
Simon Haykin and Neural Network · 2004
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Automated colour grading using colour distribution transfer
François Pitié, Anil C Kokaram, and Rozenn Dahyot · 2007
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Optimal transport: old and new
Cédric Villani · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2011
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Unidimensional and evolution methods for optimal transportation
Nicolas Bonnotte · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Manopt, a matlab toolbox for optimization on manifolds
Nicolas Boumal, Bamdev Mishra, P-A Absil, and Rodolphe Sepulchre · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Sliced and radon Wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
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The empirical distribution function and the histogram
Rui Castro · 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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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Sliced Wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K Rohde · 2016
Cited alongside, same era.
Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Cited alongside, same era.
Wasserstein variational inference
Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Marcel AJ van Gerven, and Eric Maris · 2018
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Generative modeling using the sliced Wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander Schwing · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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A two-step computation of the exact GAN Wasserstein distance
Huidong Liu, GU Xianfeng, and Dimitris Samaras · 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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Improving GANs using optimal transport
Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas · 2018
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
BEGAN: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
Cited alongside, same era.
Learning generative models with Sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Cited alongside, same era.
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.
A Riemannian network for SPD matrix learning
Zhiwu Huang and Luc Van Gool · 2017
Cited alongside, same era.
Optimal mass transport: Signal processing and machine-learning applications
Soheil Kolouri, Se Rim Park, Matthew Thorpe, Dejan Slepcev, and Gustavo K Rohde · 2017
Cited alongside, same era.
Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Video-to-video synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
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Improving the improved training of Wasserstein GANs: A consistency term and its dual effect
Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, and Liqiang Wang · 2018
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Wasserstein divergence for GANs
Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, and Luc Van Gool · 2018
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Sliced wasserstein auto-encoders
Soheil Kolouri, Phillip E Pope, Charles E Martin, and Gustavo K Rohde · 2019
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