Boosting Adversarial Training via Fisher-Rao Norm-based Regularization

arXiv:2403.17520v1 Announce Type: new
Abstract: Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet, mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First, We leverage the Fisher-Rao norm, a geometrically invariant metric for model complexity, to establish the non-trivial bounds of the Cross-Entropy Loss-based Rademacher complexity for a ReLU-activated Multi-Layer Perceptron. Then we generalize a …

adversarial adversarial training arxiv boosting cs.cv cs.lg fisher norm regularization training type via