  Though convolutional neural networks (CNNs) have demonstrated remarkable
ability in learning discriminative features, they often generalize poorly to
unseen domains. Domain generalization aims to address this problem by learning
from a set of source domains a model that is generalizable to any unseen
domain. In this paper, a novel approach is proposed based on probabilistically
mixing instance-level feature statistics of training samples across source
domains. Our method, termed MixStyle, is motivated by the observation that
visual domain is closely related to image style (e.g., photo vs.~sketch
images). Such style information is captured by the bottom layers of a CNN where
our proposed style-mixing takes place. Mixing styles of training instances
results in novel domains being synthesized implicitly, which increase the
domain diversity of the source domains, and hence the generalizability of the
trained model. MixStyle fits into mini-batch training perfectly and is
extremely easy to implement. The effectiveness of MixStyle is demonstrated on a
wide range of tasks including category classification, instance retrieval and
reinforcement learning.
