Sliced Wasserstein generative models

• 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: Deep Learning; Image and Video Synthesis; Optimization Methods; Databases and Information Systems; Graphics and Human Computer Interfaces
• 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
• Institution: Singapore Management University

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)...