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conference paper · International Joint Conference on Artificial Intelligence · 2021

Recent Advances in Adversarial Training for Adversarial Robustness

Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang

Cited by 702Open access

Abstract

Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled. Finally, we present potential future directions.

Cite this paper

APA 7

Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021). Recent Advances in Adversarial Training for Adversarial Robustness. International Joint Conference on Artificial Intelligence, 4312–4321. https://doi.org/10.24963/ijcai.2021/591

MLA 9

Bai, Tao, et al. “Recent Advances in Adversarial Training for Adversarial Robustness.” International Joint Conference on Artificial Intelligence, 2021, pp. 4312–21, https://doi.org/10.24963/ijcai.2021/591.

Chicago

Bai, Tao, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021. “Recent Advances in Adversarial Training for Adversarial Robustness.” International Joint Conference on Artificial Intelligence: 4312–21. https://doi.org/10.24963/ijcai.2021/591.

Harvard

Bai, T. et al. (2021) ‘Recent Advances in Adversarial Training for Adversarial Robustness’, International Joint Conference on Artificial Intelligence, pp. 4312–4321. Available at: https://doi.org/10.24963/ijcai.2021/591.

BibTeX
@article{bai2021recent,
  title = {{Recent Advances in Adversarial Training for Adversarial Robustness}},
  author = {Bai, Tao and Luo, Jinqi and Zhao, Jun and Wen, Bihan and Wang, Qian},
  journal = {International Joint Conference on Artificial Intelligence},
  pages = {4312--4321},
  year = {2021},
  doi = {10.24963/ijcai.2021/591}
}

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