Batch Normalization

Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to fast convergence. BN is observed to increase the model accuracy while at the cost of adversarial robustness. We conjecture that the increased adversarial …

Revisiting Batch Normalization for Improving Corruption Robustness

The performance of DNNs trained on clean images has been shown to decrease when the test images have common corruptions. In this work, we interpret corruption robustness as a domain shift and propose to rectify batch normalization (BN) statistics for …