On characterizing optimal Wasserstein GAN solutions for non-Gaussian data (Conference Paper) | NSF PAGES

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Citation Details

This content will become publicly available on June 25, 2024

On characterizing optimal Wasserstein GAN solutions for non-Gaussian data

Award ID(s):: 2109002

NSF-PAR ID:: 10448727

Author(s) / Creator(s):: Huang, Yu-Jui; Lin, Shih-Chun; Huang, Yu-Chih; Lyu, Kuan-Hui; Shen, Hsin-Hua; Lin, Wan-Yi

Date Published:: 2023-06-25

Journal Name:: 2023 IEEE International Symposium on Information Theory (ISIT)

Page Range / eLocation ID:: 909 to 914

Format(s):: Medium: X

Sponsoring Org:: National Science Foundation

Free Publicly Accessible Full Text
This content will become publicly available on June 25, 2024
Conference Paper:
https://doi.org/10.1109/ISIT54713.2023.10206785