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Domain generalization approaches aim to learn a domain invariant predictionmodel for unknown target domains from multiple training source domains withdifferent distributions. Significant efforts have recently been committed tobroad domain generalization, which is a challenging and topical problem inmachine learning and computer vision communities. Most previous domaingeneralization approaches assume that the conditional distribution across thedomains remain the same across the source domains and learn a domain invariantmodel by minimizing the marginal distributions. However, the assumption of astable conditional distribution of the training source domains does not reallyhold in practice. The hyperplane learned from the source domains will easilymisclassify samples scattered at the boundary of clusters or far from theircorresponding class centres. To address the above two drawbacks, we propose adiscriminative domain-invariant adversarial network (DDIAN) for domaingeneralization. The discriminativeness of the features are guaranteed through adiscriminative feature module and domain-invariant features are guaranteedthrough the global domain and local sub-domain alignment modules. Extensiveexperiments on several benchmarks show that DDIAN achieves better prediction onunseen target data during training compared to state-of-the-art domaingeneralization approaches.

Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization