SA-MDRAD: sample-adaptive multi-teacher dynamic rectification adversarial distillation
Author:
Funder
National Key Research and Development Program of China
Major Science and Technology Project of Henan Province in China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01416-7.pdf
Reference54 articles.
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3. Bojia Z, Shihao Z, Xingjun M, Yu-Gang J. (2021). Revisiting adversarial robustness distillation: Robust soft labels make student better. Paper presented at the 2021 IEEE/CVF International Conference on Computer Vision(ICCV 2021), Montreal, QC, Canada. https://doi.org/10.48550/arXiv.2108.07969
4. Cao G, Wang Z, Dong X, Zhang Z, Guo H, Qin Z, Ren K(2022)Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training.Preprint. https://doi.org/10.48550/arXiv.2206.02158
5. Carlini N, Wagner D. (2017). Towards evaluating the robustness of neural networks. Paper presented at the Proceedings - IEEE Symposium on Security and Privacy. https://doi.org/10.1109/SP.2017.49, arXiv:1608.04644
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