Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization

Understand

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.