  CNNs perform remarkably well when the training and test distributions are
i.i.d, but unseen image corruptions can cause a surprisingly large drop in
performance. In various real scenarios, unexpected distortions, such as random
noise, compression artefacts, or weather distortions are common phenomena.
Improving performance on corrupted images must not result in degraded i.i.d
performance - a challenge faced by many state-of-the-art robust approaches.
Image corruption types have different characteristics in the frequency spectrum
and would benefit from a targeted type of data augmentation, which, however, is
often unknown during training. In this paper, we introduce a mixture of two
expert models specializing in high and low-frequency robustness, respectively.
Moreover, we propose a new regularization scheme that minimizes the total
variation (TV) of convolution feature-maps to increase high-frequency
robustness. The approach improves on corrupted images without degrading
in-distribution performance. We demonstrate this on ImageNet-C and also for
real-world corruptions on an automotive dataset, both for object classification
and object detection.
