FeatsFlow: Traceable representation learning based on normalizing flows-Reference-Cited by-同舟云学术

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FeatsFlow: Traceable representation learning based on normalizing flows

Published:2023-11 Issue: Volume:126 Page:107151
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Zhang Wenwen^ORCID,Pei Zhao,Wang Fei-Yue

Funder

Natural Science Foundation for Young Scientists of Shanxi Province

Shaanxi Normal University

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference72 articles.

1. The cramer distance as a solution to biased wasserstein gradients;Bellemare,2018

2. Representation learning: A review and new perspectives;Bengio;IEEE Trans. Pattern Anal. Mach. Intell.,2013

3. Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A., 2018. Demystifying MMD GANs. In: International Conference on Learning Representations.

4. ReduNet: A white-box deep network from the principle of maximizing rate reduction;Chan;J. Mach. Learn. Res.,2022

5. Residual flows for invertible generative modeling;Chen;Adv. Neural Inf. Process. Syst.,2019

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