  Single domain generalization is a challenging case of model generalization,
where the models are trained on a single domain and tested on other unseen
domains. A promising solution is to learn cross-domain invariant
representations by expanding the coverage of the training domain. These methods
have limited generalization performance gains in practical applications due to
the lack of appropriate safety and effectiveness constraints. In this paper, we
propose a novel learning framework called progressive domain expansion network
(PDEN) for single domain generalization. The domain expansion subnetwork and
representation learning subnetwork in PDEN mutually benefit from each other by
joint learning. For the domain expansion subnetwork, multiple domains are
progressively generated in order to simulate various photometric and geometric
transforms in unseen domains. A series of strategies are introduced to
guarantee the safety and effectiveness of the expanded domains. For the domain
invariant representation learning subnetwork, contrastive learning is
introduced to learn the domain invariant representation in which each class is
well clustered so that a better decision boundary can be learned to improve
it's generalization. Extensive experiments on classification and segmentation
have shown that PDEN can achieve up to 15.28% improvement compared with the
state-of-the-art single-domain generalization methods.
