.png){kind=icon}
.png){kind=icon}
Return
Bridged adversarial training
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2023.08.024.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Adversarial robustness is considered a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different characteristics in terms of margin and smoothness, even though they show similar robustness. Inspired by the observation, we investigate the effect of different regularizers and discover the negative effect of the smoothness regularizer on maximizing the margin. Based on the analyses, we propose a new method called bridged adversarial training that mitigates the negative effect by bridging the gap between clean and adversarial examples. We provide theoretical and empirical evidence that the proposed method provides stable and better robustness, especially for large perturbations.& COPY; 2023 Elsevier Ltd. All rights reserved.
Keywords:
Neural networks
Adversarial robustness
Adversarial training
Adversarial defense
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
7.8K
Citations:
3.0W
View more organizations