FFT-based Selection and Optimization of Statistics for Robust
  Recognition of Severely Corrupted Images
Elena Camuffo Umberto Michieli Jijoong Moon Daehyun Kim Mete Ozay
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.