先前Lipschitz连续性对贝叶斯神经网络的对抗鲁棒性的影响

Jan, 2021

The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

Arno Blaas, Stephen J. Roberts

TL;DR本文考察了贝叶斯神经网络的对抗鲁棒性，并研究了模型选择对其稳健性的影响，发现先验方差确实对其对抗鲁棒性有影响。

Abstract

It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for bayes