  As a recent noticeable topic, domain generalization (DG) aims to first learn
a generic model on multiple source domains and then directly generalize to an
arbitrary unseen target domain without any additional adaption. In previous DG
models, by generating virtual data to supplement observed source domains, the
data augmentation based methods have shown its effectiveness. To simulate the
possible unseen domains, most of them enrich the diversity of original data via
image-level style transformation. However, we argue that the potential styles
are hard to be exhaustively illustrated and fully augmented due to the limited
referred styles, leading the diversity could not be always guaranteed. Unlike
image-level augmentation, we in this paper develop a simple yet effective
feature-based style randomization module to achieve feature-level augmentation,
which can produce random styles via integrating random noise into the original
style. Compared with existing image-level augmentation, our feature-level
augmentation favors a more goal-oriented and sample-diverse way. Furthermore,
to sufficiently explore the efficacy of the proposed module, we design a novel
progressive training strategy to enable all parameters of the network to be
fully trained. Extensive experiments on three standard benchmark datasets,
i.e., PACS, VLCS and Office-Home, highlight the superiority of our method
compared to the state-of-the-art methods.
