Effective Single-Step Adversarial Training With Energy-Based Models-Reference-Cited by-同舟云学术

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Effective Single-Step Adversarial Training With Energy-Based Models

Published:2024 Issue: Volume: Page:1-12
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Tang Keke¹^ORCID,Lou Tianrui²^ORCID,Peng Weilong³^ORCID,Chen Nenglun⁴^ORCID,Shi Yawen¹^ORCID,Wang Wenping⁵^ORCID

Affiliation:

1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

2. School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China

3. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

4. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China

5. Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA

Funder

National Natural Science Foundation of China

Guangdong Basic and Applied Basic Research Foundation

Science and Technology Program of Guangzhou

Program of Guangzhou University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/7777658/10485467.pdf?arnumber=10485467

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1. Negation of permutation mass function in random permutation sets theory for uncertain information modeling;Complex & Intelligent Systems;2024-07-30

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