Improving Adversarial Robustness of Attribution via Implicit Regularization 事件

Improving Adversarial Robustness of Attribution via Implicit Regularization arXiv:2605.29983v1 Announce Type: cross Abstract: The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typically rely on computationally expensive explicit regularization. In this work, we show that attribution robustness can arise implicitly from the learning dynamics of standard stochastic gradient descent. We theoretically motiva