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.

翻译：域泛化（DG）关注如何将多个源域（训练时可获取）中的域不变知识迁移至先前未见的目标域。这要求每个类别在多个域中均有表达，以便学习算法能够打破域与类别之间的虚假关联。然而在实际场景中，类别常与特定域关联（即仅在特定域中表达），这导致这些类别的泛化性能极差。本研究旨在通过从多源域表达类别（域共享类别）迁移域不变知识，为域关联类别学习可泛化表征。为此，我们向学界提出该任务，并设计了面向域关联DG的公平对比特征空间正则化算法FOND。在主流DG任务基准上的严格可重复实验表明，我们的方法及其变体能够实现域关联类别的最优DG性能。我们还针对提升域关联类别泛化性的数据条件提出实践洞见，以应对现实场景中的数据稀缺问题。