Domain Generalization for Domain-Linked Classes
Kimathi Kaai Saad Hossain Sirisha Rambhatla
Domain generalization (DG) focuses on transferring domain-invariant knowledge
from multiple source domains (available at train time) to an, a priori, unseen
target domain(s). This requires a class to be expressed in multiple domains for
the learning algorithm to break the spurious correlations between domain and
class. However, in the real-world, classes may often be domain-linked, i.e.
expressed only in a specific domain, which leads to extremely poor
generalization performance for these classes. In this work, we aim to learn
generalizable representations for these domain-linked classes by transferring
domain-invariant knowledge from classes expressed in multiple source domains
(domain-shared classes). To this end, we introduce this task to the community
and propose a Fair and cONtrastive feature-space regularization algorithm for
Domain-linked DG, FOND. Rigorous and reproducible experiments with baselines
across popular DG tasks demonstrate our method and its variants' ability to
accomplish state-of-the-art DG results for domain-linked classes. We also
provide practical insights on data conditions that increase domain-linked class
generalizability to tackle real-world data scarcity.