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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks.

, , и . ICLR, OpenReview.net, (2020)

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Tianyu Zhao

Tianyu Chen

Vincent Pang

Tianyu Chen

Tianyu Wu

 

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Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows., , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 8565-8584. PMLR, (2023)Max-Mahalanobis Linear Discriminant Analysis Networks., , и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 4013-4022. PMLR, (2018)Finetuning Text-to-Image Diffusion Models for Fairness., , , , , и . CoRR, (2023)Black-box Detection of Backdoor Attacks with Limited Information and Data., , , , , , и . ICCV, стр. 16462-16471. IEEE, (2021)Improving Adversarial Robustness via Promoting Ensemble Diversity., , , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 4970-4979. PMLR, (2019)Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks., , и . ICLR, OpenReview.net, (2020)Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart., , , , , , , и . CVPR, стр. 15202-15212. IEEE, (2022)Efficient Learning of Generative Models via Finite-Difference Score Matching., , , , , и . NeurIPS, (2020)Crafting Heavy-Tails in Weight Matrix Spectrum without Gradient Noise., , , , и . CoRR, (2024)Weak-to-Strong Jailbreaking on Large Language Models., , , , , , и . CoRR, (2024)