  Neural networks do not generalize well to unseen data with domain shifts -- a
longstanding problem in machine learning and AI. To overcome the problem, we
propose MixStyle, a simple plug-and-play, parameter-free module that can
improve domain generalization performance without the need to collect more data
or increase model capacity. The design of MixStyle is simple: it mixes the
feature statistics of two random instances in a single forward pass during
training. The idea is grounded by the finding from recent style transfer
research that feature statistics capture image style information, which
essentially defines visual domains. Therefore, mixing feature statistics can be
seen as an efficient way to synthesize new domains in the feature space, thus
achieving data augmentation. MixStyle is easy to implement with a few lines of
code, does not require modification to training objectives, and can fit a
variety of learning paradigms including supervised domain generalization,
semi-supervised domain generalization, and unsupervised domain adaptation. Our
experiments show that MixStyle can significantly boost out-of-distribution
generalization performance across a wide range of tasks including image
recognition, instance retrieval and reinforcement learning.
