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Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality.
On the efficiency of the Sinkhorn and Greenkhorn algorithms and their acceleration for optimal transport
T. Lin, N. Ho, and M. I. Jordan · 1906
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The inversion problem and applications of the generalized Radon transform
Gregory Beylkin · 1984
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Partial Radon transforms
Zhi-Pei Liang and David C Munson · 1997
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N-distances and their applications
Lev Borisovich Klebanov, Viktor Beneš, and Ivan Saxl · 2005
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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, Geoffrey Hinton, et al · 2009
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Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
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The Radon transform on r n
Sigurdur Helgason · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Adam: A method for stochastic optimization
Diederik P 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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On the rate of convergence in Wasserstein distance of the empirical measure
N. Fournier and A. Guillin · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Jason Altschuler, Jonathan Niles-Weed, and Philippe Rigollet · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 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
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Multilevel clustering via Wasserstein means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, and Dinh Phung · 2017
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
Cited alongside, same era.
Generative modeling using the sliced Wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander G Schwing · 2018
Cited alongside, same era.
Learning generative models with Sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2018
Cited alongside, same era.
Improving GANs using optimal transport
Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas · 2018
Cited alongside, same era.
Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning
Morgan A Schmitz, Matthieu Heitz, Nicolas Bonneel, Fred Ngole, David Coeurjolly, Marco Cuturi, Gabriel Peyré, and Jean-Luc Starck · 2018
Cited alongside, same era.
Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
Approximate Bayesian computation with the sliced-Wasserstein distance
Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, and Umut Şimşekli · 2020
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Computational optimal transport, 2020
Gabriel Peyré and Marco Cuturi · 2020
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Sliced score matching: A scalable approach to density and score estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2020
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Predicting cell lineages using autoencoders and optimal transport
Karren Dai Yang, Karthik Damodaran, Saradha Venkatachalapathy, Ali C Soylemezoglu, GV Shivashankar, and Caroline Uhler · 2020
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Sliced-Wasserstein gradient flows
Clément Bonet, Nicolas Courty, François Septier, and Lucas Drumetz · 2021
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Sliced iterative normalizing flows
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Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
Cited alongside, same era.
Spot: sliced partial optimal transport
Nicolas Bonneel and David Coeurjolly · 2019
Cited alongside, same era.
Max-sliced Wasserstein distance and its use for GANs
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander G Schwing · 2019
Cited alongside, same era.
Sliced Wasserstein discrepancy for unsupervised domain adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
Cited alongside, same era.
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stöter · 2019
Cited alongside, same era.
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Gonzalo Mena and Jonathan Weed · 2019
Cited alongside, same era.
Biwei Dai and Uros Seljak · 2021
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Sliced mutual information: A scalable measure of statistical dependence
Ziv Goldfeld and Kristjan Greenewald · 2021
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Lamda: Label matching deep domain adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, and Dinh Phung · 2021
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Run-sort-rerun: Escaping batch size limitations in sliced Wasserstein generative models
Jose Lezama, Wei Chen, and Qiang Qiu · 2021
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Pooling by sliced-Wasserstein embedding
Navid Naderializadeh, Joseph Comer, Reed Andrews, Heiko Hoffmann, and Soheil Kolouri · 2021
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Fast approximation of the sliced-Wasserstein distance using concentration of random projections
Kimia Nadjahi, Alain Durmus, Pierre E Jacob, Roland Badeau, and Umut Simsekli · 2021
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Differentially private sliced Wasserstein distance
Alain Rakotomamonjy and Ralaivola Liva · 2021
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Vocabulary learning via optimal transport for neural machine translation
Jingjing Xu, Hao Zhou, Chun Gan, Zaixiang Zheng, and Lei Li · 2021
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Sliced Wasserstein variational inference
Mingxuan Yi and Song Liu · 2021
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Sliced optimal partial transport
Yikun Bai, Bernard Schmitzer, Mathew Thorpe, and Soheil Kolouri · 2022
Closest in time.
Clément Bonet, Paul Berg, Nicolas Courty, François Septier, Lucas Drumetz, and Minh-Tan Pham · 2022
Closest in time.
Augmented sliced Wasserstein distances
Xiongjie Chen, Yongxin Yang, and Yunpeng Li · 2022
Closest in time.
Generative modeling with optimal transport maps
litu Rout, Alexander Korotin, and Evgeny Burnaev · 2022
Closest in time.
Statistical, robustness, and computational guarantees for sliced wasserstein distances
Sloan Nietert, Ziv Goldfeld, Ritwik Sadhu, and Kengo Kato · 2022
Closest in time.
Distributional convergence of the sliced wasserstein process
Jiaqi Xi and Jonathan Niles-Weed · 2022
Closest in time.