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A gradual, semi-discrete approach to generative network training via
  explicit Wasserstein minimization
v1v2 (latest)

A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

International Conference on Machine Learning (ICML), 2019
8 June 2019
Yucheng Chen
Matus Telgarsky
Chao Zhang
Bolton Bailey
Daniel J. Hsu
Jian-wei Peng
    GANOT
ArXiv (abs)PDFHTML

Papers citing "A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization"

13 / 13 papers shown
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Ferdinand Genans
Antoine Godichon-Baggioni
François-Xavier Vialard
Olivier Wintenberger
136
0
0
31 Oct 2025
On optimal solutions of classical and sliced Wasserstein GANs with non-Gaussian data
On optimal solutions of classical and sliced Wasserstein GANs with non-Gaussian data
Yu-Jui Huang
Hsin-Hua Shen
Yu-Chih Huang
Wan-Yi Lin
Shih-Chun Lin
GAN
200
0
0
08 Sep 2025
HOTS3D: Hyper-Spherical Optimal Transport for Semantic Alignment of Text-to-3D Generation
HOTS3D: Hyper-Spherical Optimal Transport for Semantic Alignment of Text-to-3D Generation
Zezeng Li
Weimin Wang
WenHai Li
Na Lei
Na Lei
Xianfeng Gu
OTDiffM
336
0
0
19 Jul 2024
ENOT: Expectile Regularization for Fast and Accurate Training of Neural
  Optimal Transport
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
N. Buzun
Maksim Bobrin
Dmitry V. Dylov
OTOOD
369
5
0
06 Mar 2024
Fair and Optimal Classification via Post-Processing
Fair and Optimal Classification via Post-ProcessingInternational Conference on Machine Learning (ICML), 2022
Ruicheng Xian
Lang Yin
Han Zhao
FaML
421
46
0
03 Nov 2022
Wasserstein Iterative Networks for Barycenter Estimation
Wasserstein Iterative Networks for Barycenter EstimationNeural Information Processing Systems (NeurIPS), 2022
Alexander Korotin
Vage Egiazarian
Lingxiao Li
Evgeny Burnaev
380
32
0
28 Jan 2022
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2
  Benchmark
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 BenchmarkNeural Information Processing Systems (NeurIPS), 2021
Alexander Korotin
Lingxiao Li
Aude Genevay
Justin Solomon
Alexander N. Filippov
Evgeny Burnaev
OT
382
99
0
03 Jun 2021
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein
  Distance)
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Jan Stanczuk
Christian Etmann
L. Kreusser
Carola-Bibiane Schönlieb
GAN
341
52
0
02 Mar 2021
On the Existence of Optimal Transport Gradient for Learning Generative
  Models
On the Existence of Optimal Transport Gradient for Learning Generative Models
Antoine Houdard
Arthur Leclaire
Nicolas Papadakis
Julien Rabin
OTGAN
139
7
0
10 Feb 2021
A Generative Model for Texture Synthesis based on Optimal Transport
  between Feature Distributions
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
Antoine Houdard
Arthur Leclaire
Nicolas Papadakis
Julien Rabin
OTDiffM
238
26
0
19 Jun 2020
Implicit competitive regularization in GANs
Implicit competitive regularization in GANsInternational Conference on Machine Learning (ICML), 2019
Florian Schäfer
Hongkai Zheng
Anima Anandkumar
GAN
264
34
0
13 Oct 2019
Optimal transport mapping via input convex neural networks
Optimal transport mapping via input convex neural networksInternational Conference on Machine Learning (ICML), 2019
Ashok Vardhan Makkuva
Amirhossein Taghvaei
Sewoong Oh
Jason D. Lee
OT
332
231
0
28 Aug 2019
Regularized Wasserstein Means for Aligning Distributional Data
Regularized Wasserstein Means for Aligning Distributional Data
Liang Mi
Wen Zhang
Yalin Wang
OOD
176
7
0
02 Dec 2018
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