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.

该研究论文提出了一个公平且对比度特征空间正则化的算法，名为 FOND，旨在学习可以转移源领域知识的、用于表示在特定领域中仅存在的类的广义表示。实验结果表明该算法在提高特定领域类别的泛化表现上取得了最先进的效果，并提供了可行的数据增强实用技巧。