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Adversarial Training with Complementary Labels: On the Benefit of
  Gradually Informative Attacks

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

1 November 2022
Jianan Zhou
Jianing Zhu
Jingfeng Zhang
Tongliang Liu
Gang Niu
Bo Han
Masashi Sugiyama
    AAML
ArXiv (abs)PDFHTMLGithub (14★)

Papers citing "Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks"

3 / 3 papers shown
Title
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting
Yibo Xu
Dawei Zhou
Decheng Liu
N. Wang
AAML
92
0
0
06 Feb 2025
Boosting Single Positive Multi-label Classification with Generalized
  Robust Loss
Boosting Single Positive Multi-label Classification with Generalized Robust Loss
Yanxi Chen
Chunxiao Li
Xinyang Dai
Jinhuan Li
Weiyu Sun
Yiming Wang
Renyuan Zhang
Tinghe Zhang
Bo Wang
82
2
0
06 May 2024
Learning with Complementary Labels Revisited: The
  Selected-Completely-at-Random Setting Is More Practical
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Wei Wang
Takashi Ishida
Yu Zhang
Gang Niu
Masashi Sugiyama
102
5
0
27 Nov 2023
1