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We introduce Domain-specific Masks for Generalization, a model for improvingboth in-domain and out-of-domain generalization performance. For domaingeneralization, the goal is to learn from a set of source domains to produce asingle model that will best generalize to an unseen target domain. As such,many prior approaches focus on learning representations which persist acrossall source domains with the assumption that these domain agnosticrepresentations will generalize well. However, often individual domains containcharacteristics which are unique and when leveraged can significantly aidin-domain recognition performance. To produce a model which best generalizes toboth seen and unseen domains, we propose learning domain specific masks. Themasks are encouraged to learn a balance of domain-invariant and domain-specificfeatures, thus enabling a model which can benefit from the predictive power ofspecialized features while retaining the universal applicability ofdomain-invariant features. We demonstrate competitive performance compared tonaive baselines and state-of-the-art methods on both PACS and DomainNet.

Learning to Balance Specificity and Invariance for In and Out of Domain Generalization