Sliced Wasserstein generative models

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD)...

Full description

Saved in:
Bibliographic Details
Main Authors: WU, Jiqing, HUANG, Zhiwu, ACHARYA, Dinesh, LI, Wen, THOMA, Janine, PAUDEL, Danda Pani, VAN GOOL, Luc
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6401
https://ink.library.smu.edu.sg/context/sis_research/article/7404/viewcontent/Sliced_Wasserstein_Generative_Models.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English