Boosting adversarial robustness via self-paced adversarial training.

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作者： Lirong,He ^[1] ; Qingzhong,Ai ^[2] ; Xincheng,Yang ^[2] ; Yazhou,Ren ^[2] ; Qifan,Wang ^[3] ; Zenglin,Xu ^[4]

作者单位： School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. ^[1] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. ^[2] Meta AI, Menlo Park, CA, USA. ^[3] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, China; Peng Cheng Lab, Shenzhen, Guangdong, China. Electronic address: xuzenglin@hit.edu.cn. ^[4]

关键词 Adversarial robustness Adversarial training Self-paced learning

主题词学习(Learning);规范(Benchmarking)

DOI 10.1016/j.neunet.2023.08.063

PMID 37729786

发布时间 2023-10-23

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Neural networks

2023年167卷

706-714页

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Boosting adversarial robustness via self-paced adversarial training.

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Boosting adversarial robustness via self-paced adversarial training.

Neural networks

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Neural networks

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