  For safety-critical applications such as autonomous driving, CNNs have to be
robust with respect to unavoidable image corruptions, such as image noise.
While previous works addressed the task of robust prediction in the context of
full-image classification, we consider it for dense semantic segmentation. We
build upon an insight from image classification that output robustness can be
improved by increasing the network-bias towards object shapes. We present a new
training schema that increases this shape bias. Our basic idea is to
alpha-blend a portion of the RGB training images with faked images, where each
class-label is given a fixed, randomly chosen color that is not likely to
appear in real imagery. This forces the network to rely more strongly on shape
cues. We call this data augmentation technique ``Painting-by-Numbers''. We
demonstrate the effectiveness of our training schema for DeepLabv3+ with
various network backbones, MobileNet-V2, ResNets, and Xception, and evaluate it
on the Cityscapes dataset. With respect to our 16 different types of image
corruptions and 5 different network backbones, we are in 74% better than
training with clean data. For cases where we are worse than a model trained
without our training schema, it is mostly only marginally worse. However, for
some image corruptions such as images with noise, we see a considerable
performance gain of up to 25%.
