Adversarial training has been demonstrated to be one of the most effective remedies for
defending adversarial examples, yet it often suffers from the huge robustness generalization …

Adversarial training has been demonstrated to be one of the most effective remedies for
defending adversarial examples, yet it often suffers from the huge robustness generalization …

Adversarial training is usually difficult to optimize. This paper provides conceptual and
analytic insights into the difficulty of adversarial training via a simple theoretical study, where …

Adversarial training and its variants have become the de facto standard for combatting
against adversarial attacks in machine learning models. In this paper, we seek insight into …

This paper mathematically derives an analytic solution of the adversarial perturbation on a
ReLU network, and theoretically explains the difficulty of adversarial training. Specifically …

Over the last decade, deep neural networks (DNNs) have become an increasingly popular
choice for researchers looking to take on previously unsolved problems. With the popularity …