Jun 13, 2022 � Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and�...

Adversarial training (AT) and its variants have spearheaded progress in improving neural net- work robustness to adversarial perturbations and.

Adversarial training (AT) is currently the most effective method to improve the adversarial robustness of neural networks. AT and its variants have created�...

Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and common�...

Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations � 1 code implementation�...

Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations � Environment (named DL_env)�...

This work shows that, when used with an appropriately selected perturbation radius, adversarial training can serve as a strong baseline against common�...

Both transduction and rejection have emerged as important techniques for defending against adversarial perturbations. A recent work by Tram\`er showed that,�...

Missing: Techniques | Show results with:Techniques

Mar 14, 2024 � Adversarial training has proven effective in improving the robustness of DNNs against a wide range of adversarial perturbations.