  Convolutional neural networks (CNNs) learn to extract representations of
complex features, such as object shapes and textures to solve image recognition
tasks. Recent work indicates that CNNs trained on ImageNet are biased towards
features that encode textures and that these alone are sufficient to generalize
to unseen test data from the same distribution as the training data but often
fail to generalize to out-of-distribution data. It has been shown that
augmenting the training data with different image styles decreases this texture
bias in favor of increased shape bias while at the same time improving
robustness to common corruptions, such as noise and blur. Commonly, this is
interpreted as shape bias increasing corruption robustness. However, this
relationship is only hypothesized. We perform a systematic study of different
ways of composing inputs based on natural images, explicit edge information,
and stylization. While stylization is essential for achieving high corruption
robustness, we do not find a clear correlation between shape bias and
robustness. We conclude that the data augmentation caused by style-variation
accounts for the improved corruption robustness and increased shape bias is
only a byproduct.
