  Domain Generalization (DG) aims to train a model, from multiple observed
source domains, in order to perform well on unseen target domains. To obtain
the generalization capability, prior DG approaches have focused on extracting
domain-invariant information across sources to generalize on target domains,
while useful domain-specific information which strongly correlates with labels
in individual domains and the generalization to target domains is usually
ignored. In this paper, we propose meta-Domain Specific-Domain Invariant
(mDSDI) - a novel theoretically sound framework that extends beyond the
invariance view to further capture the usefulness of domain-specific
information. Our key insight is to disentangle features in the latent space
while jointly learning both domain-invariant and domain-specific features in a
unified framework. The domain-specific representation is optimized through the
meta-learning framework to adapt from source domains, targeting a robust
generalization on unseen domains. We empirically show that mDSDI provides
competitive results with state-of-the-art techniques in DG. A further ablation
study with our generated dataset, Background-Colored-MNIST, confirms the
hypothesis that domain-specific is essential, leading to better results when
compared with only using domain-invariant.
