  Improving model robustness in case of corrupted images is among the key
challenges to enable robust vision systems on smart devices, such as robotic
agents. Particularly, robust test-time performance is imperative for most of
the applications. This paper presents a novel approach to improve robustness of
any classification model, especially on severely corrupted images. Our method
(FROST) employs high-frequency features to detect input image corruption type,
and select layer-wise feature normalization statistics. FROST provides the
state-of-the-art results for different models and datasets, outperforming
competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of
40.9% mCE on severe corruptions.
