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Instance adaptive adversarial training: Improved accuracy ...
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由 Y Balaji 著作2019被引用 145 次 — Abstract:Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.
Instance adaptive adversarial training: Improved accuracy ...
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由 Y Balaji 著作被引用 145 次 — Abstract: Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.
yogeshbalaji/Instance_Adaptive_Adversarial_Training
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Code accompanying out paper: "Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets"
[论文笔记]INSTANCE ADAPTIVE ADVERSARIAL TRAINING
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2019年11月19日 — 自适应训练这是PyTorch的实现NeurIPS的2020年论文《 ， 期刊版《 。 自适应训练显着提高了噪声下的深度网络的泛化能力，并增强了自我监督的表示学习。 它还 ...
Adaptive Adversarial Training Does Not Increase Recourse ...
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由 I Hardy 著作2023被引用 3 次 — We establish that the improvements in model robustness induced by adaptive adversarial training show little effect on algorithmic recourse costs.
GAAT: Group Adaptive Adversarial Training to Improve ...
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由 Y Qian 著作2022被引用 1 次 — Adversarial training is by far one of the most effective methods to improve the robustness of deep neural networks against adversarial examples.
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Towards Improved Recourse Trade-offs with Adaptive ...
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Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets, 2019. [3] Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault ...
Improving Robustness with Adaptive Weight Decay
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由 MA Ghiasi 著作2024被引用 4 次 — Balaji, Y., Goldstein, T., and Hoffman, J. Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets. arXiv preprint arXiv:1910.08051, ...
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Improving adversarial robustness by learning shared ...
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由 X Yu 著作2023被引用 16 次 — We consider the problem of improving the adversarial robustness of neural networks while retaining natural accuracy. Motivated by the multi-view information ...
[PDF] Strength-Adaptive Adversarial Training
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Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets · Y. BalajiT. GoldsteinJudy Hoffman. Computer Science. arXiv.org. 2019.
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