Title: DYNAMIC BATCH NORM STATISTICS UPDATE FOR NATURAL ROBUSTNESS. Abstract: DNNs trained on natural clean samples have been shown to perform poorly on corrupted samples,
such as noisy or blurry images. Various data augmentation methods have been recently proposed
to improve DNN’s robustness against common corruptions. Despite their success, they require
computationally expensive training and cannot be applied to off-the-shelf trained models. Recently,
updating only BatchNorm(BN) statistics of a model on a single corruption has been shown to improve
its accuracy on that corruption significantly. However, adopting the idea at inference time when the
type of corruption changes decreases the effectiveness of this method. In this paper, we harness the
Fourier domain to detect the corruption type, a challenging task in the image domain. We propose
a unified framework consisting of a corruption-detection model and BN statistics update that can
improve the corruption accuracy of any off-the-shelf trained model. We benchmark our framework
on different models and datasets. Our results demonstrate about 8% and 4% accuracy improvement
on CIFAR10-C and ImageNet-C, respectively. Furthermore, our framework can further improve the
accuracy of state-of-the-art robust models, such as AugMix and DeepAug.