  Convolutional Neural Networks (CNNs) often fail to maintain their performance
when they confront new test domains, which is known as the problem of domain
shift. Recent studies suggest that one of the main causes of this problem is
CNNs' strong inductive bias towards image styles (i.e. textures) which are
sensitive to domain changes, rather than contents (i.e. shapes). Inspired by
this, we propose to reduce the intrinsic style bias of CNNs to close the gap
between domains. Our Style-Agnostic Networks (SagNets) disentangle style
encodings from class categories to prevent style biased predictions and focus
more on the contents. Extensive experiments show that our method effectively
reduces the style bias and makes the model more robust under domain shift. It
achieves remarkable performance improvements in a wide range of cross-domain
tasks including domain generalization, unsupervised domain adaptation, and
semi-supervised domain adaptation on multiple datasets.
