  Recent studies have proven that DNNs, unlike human vision, tend to exploit
texture information rather than shape. Such texture bias is one of the factors
for the poor generalization performance of DNNs. We observe that the texture
bias negatively affects not only in-domain generalization but also
out-of-distribution generalization, i.e., Domain Generalization. Motivated by
the observation, we propose a new framework to reduce the texture bias of a
model by a novel optimization-based data augmentation, dubbed Stylized Dream.
Our framework utilizes adaptive instance normalization (AdaIN) to augment the
style of an original image yet preserve the content. We then adopt a
regularization loss to predict consistent outputs between Stylized Dream and
original images, which encourages the model to learn shape-based
representations. Extensive experiments show that the proposed method achieves
state-of-the-art performance in out-of-distribution settings on public
benchmark datasets: PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet.
