On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks

Understand

Recently, there has been an abundance of works on designing Deep Neural Networks (DNNs) that are robust to adversarial examples.

• In particular, a central question is which features of DNNs influence adversarial robustness and, therefore, can be to used to design robust DNNs.
• In this work, this problem is studied through the lens of compression which is captured by the low-rank structure of weight matrices.
• It is first shown that adversarial training tends to promote simultaneously low-rank and sparse structure in the weight matrices of neural networks.