  Domain generalization (DG) aims to generalize a model trained on multiple
source (i.e., training) domains to a distributionally different target (i.e.,
test) domain. In contrast to the conventional DG that strictly requires the
availability of multiple source domains, this paper considers a more realistic
yet challenging scenario, namely Single Domain Generalization (Single-DG),
where only one source domain is available for training. In this scenario, the
limited diversity may jeopardize the model generalization on unseen target
domains. To tackle this problem, we propose a style-complement module to
enhance the generalization power of the model by synthesizing images from
diverse distributions that are complementary to the source ones. More
specifically, we adopt a tractable upper bound of mutual information (MI)
between the generated and source samples and perform a two-step optimization
iteratively: (1) by minimizing the MI upper bound approximation for each sample
pair, the generated images are forced to be diversified from the source
samples; (2) subsequently, we maximize the MI between the samples from the same
semantic category, which assists the network to learn discriminative features
from diverse-styled images. Extensive experiments on three benchmark datasets
demonstrate the superiority of our approach, which surpasses the
state-of-the-art single-DG methods by up to 25.14%.
