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Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

, , , , , , и . ICLR, OpenReview.net, (2022)

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Yangguang Dai

Huangdong Dai

Jing Dai

Min Dai

Pengyu Dai

 

Другие публикации лиц с тем же именем

Out-of-Distribution Detection Using Neural Rendering Generative Models, , , , и . (2019)cite arxiv:1907.04572.Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker., , , , , , и . CoRR, (2023)Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?, , , , , , и . ICLR, OpenReview.net, (2022)Parameterizing Activation Functions for Adversarial Robustness., , и . SP (Workshops), стр. 80-87. IEEE, (2022)MultiRobustBench: Benchmarking Robustness Against Multiple Attacks., , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 6760-6785. PMLR, (2023)Position Paper: Beyond Robustness Against Single Attack Types., , , и . CoRR, (2024)Neural Networks with Recurrent Generative Feedback., , , , , , и . NeurIPS, (2020)PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses., , , , , и . USENIX Security Symposium, USENIX Association, (2024)PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses., , , , , и . CoRR, (2023)Formulating Robustness Against Unforeseen Attacks., , и . NeurIPS, (2022)