  When designing a semantic segmentation module for a practical application,
such as autonomous driving, it is crucial to understand the robustness of the
module with respect to a wide range of image corruptions. While there are
recent robustness studies for full-image classification, we are the first to
present an exhaustive study for semantic segmentation, based on the
state-of-the-art model DeepLabv3+. To increase the realism of our study, we
utilize almost 400,000 images generated from Cityscapes, PASCAL VOC 2012, and
ADE20K. Based on the benchmark study, we gain several new insights. Firstly,
contrary to full-image classification, model robustness increases with model
performance, in most cases. Secondly, some architecture properties affect
robustness significantly, such as a Dense Prediction Cell, which was designed
to maximize performance on clean data only.
