  Domain generalization approaches aim to learn a domain invariant prediction
model for unknown target domains from multiple training source domains with
different distributions. Significant efforts have recently been committed to
broad domain generalization, which is a challenging and topical problem in
machine learning and computer vision communities. Most previous domain
generalization approaches assume that the conditional distribution across the
domains remain the same across the source domains and learn a domain invariant
model by minimizing the marginal distributions. However, the assumption of a
stable conditional distribution of the training source domains does not really
hold in practice. The hyperplane learned from the source domains will easily
misclassify samples scattered at the boundary of clusters or far from their
corresponding class centres. To address the above two drawbacks, we propose a
discriminative domain-invariant adversarial network (DDIAN) for domain
generalization. The discriminativeness of the features are guaranteed through a
discriminative feature module and domain-invariant features are guaranteed
through the global domain and local sub-domain alignment modules. Extensive
experiments on several benchmarks show that DDIAN achieves better prediction on
unseen target data during training compared to state-of-the-art domain
generalization approaches.
