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

We introduce Domain-specific Masks for Generalization, a model for improving both in-___domain and out-of-___domain generalization performance. For ___domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target ___domain. As such, many prior approaches focus on learning representations which persist across all source domains with the assumption that these ___domain agnostic representations will generalize well. However, often individual domains contain characteristics which are unique and when leveraged can significantly aid in-___domain recognition performance. To produce a model which best generalizes to both seen and unseen domains, we propose learning ___domain specific masks. The masks are encouraged to learn a balance of ___domain-invariant and ___domain-specific features, thus enabling a model which can benefit from the predictive power of specialized features while retaining the universal applicability of ___domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet.