Enhancing Adversarial Robustness via Uncertainty-Aware Distributional
Adversarial Training
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional
Adversarial Training
Despite remarkable achievements in deep learning across various domains, its inherent vulnerability to adversarial examples still remains a critical concern for practical deployment. Adversarial training has emerged as one of the most effective defensive techniques for improving model robustness against such malicious inputs. However, existing adversarial training schemes often lead …