  Domain generalization (DG) aims to improve the generalization performance for
an unseen target domain by using the knowledge of multiple seen source domains.
Mainstream DG methods typically assume that the domain label of each source
sample is known a priori, which is challenged to be satisfied in many
real-world applications. In this paper, we study a practical problem of
compound DG, which relaxes the discrete domain assumption to the mixed source
domains setting. On the other hand, current DG algorithms prioritize the focus
on semantic invariance across domains (one-vs-one), while paying less attention
to the holistic semantic structure (many-vs-many). Such holistic semantic
structure, referred to as meta-knowledge here, is crucial for learning
generalizable representations. To this end, we present Compound Domain
Generalization via Meta-Knowledge Encoding (COMEN), a general approach to
automatically discover and model latent domains in two steps. Firstly, we
introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize
the multi-modal underlying distributions, thereby dividing the mixture of
source domains into latent clusters. Secondly, we harness the prototype
representations, the centroids of classes, to perform relational modeling in
the embedding space with two parallel and complementary modules, which
explicitly encode the semantic structure for the out-of-distribution
generalization. Experiments on four standard DG benchmarks reveal that COMEN
exceeds the state-of-the-art performance without the need of domain
supervision.
