Bringing Equity to Classification: Domain Generalization for Domain-Linked Classes.

Domain generalization (DG) focuses on transferring domain-invariant knowledge from multiple source (training) domains to an a priori unseen target domain(s). This task implicitly requires that classes of interest are expressed in multiple sources (domain-shared) to break spurious domain-class correlations. However, real-world data scarcity challenges may often result in classes present in only a specific domain (domain-linked), which we show leads to extremely poor generalization. In this work, we introduce the domain-linked DG task to the community and develop a methodology to learn useful domain-invariant representations from domain-shared classes for domain-linked ones. Specifically, we propose FOND, a Fairness-inspired and cONtrastive learning objective for Domain-linked DG. Rigorous and reproducible experimental results communicate that FOND accomplishes state-of-the-art improvements for domain-linked classes, given a sufficient number of domain-shared classes and with minimal performance trade-offs. Complementary to these contributions, we theoretically analyze this task and provide practical insights for domain-linked class generalizability.