Domain adaptation aims to mitigate distribution shifts among different domains. However, traditional formulations are mostly limited to categorical domains, greatly simplifying nuanced domain relationships in the real world. In this work, we tackle a generalization with taxonomy-structured domains, which formalizes domains with nested, hierarchical similarity structures such as animal species and product catalogs. We build on the classic adversarial framework and introduce a novel taxonomist, which competes with the adversarial discriminator to preserve the taxonomy information. The equilibrium recovers the classic adversarial domain adaptation's solution if given a non-informative domain taxonomy (e.g., a flat taxonomy where all leaf nodes connect to the root node) while yielding non-trivial results with other taxonomies. Empirically, our method achieves state-of-the-art performance on both synthetic and real-world datasets with successful adaptation. Code is available at https://github.com/Wang-ML-Lab/TSDA.

翻译：领域自适应旨在缓解不同领域之间的分布偏移。然而，传统方法通常局限于离散类别领域的设定，极大简化了真实世界中领域间复杂的关联关系。本研究针对一种泛化问题——具有分类结构的领域——展开，该结构将领域形式化为嵌套的层次化相似性结构（例如动物物种和产品目录）。我们在经典对抗性框架基础上提出新颖的分类器，该分类器与对抗性判别器竞争以保留分类结构信息。当领域分类结构为非信息性结构（例如所有叶节点直接连接根节点的扁平分类结构）时，该框架的平衡态可回归经典对抗性领域自适应的解，而其他分类结构则能产生非平凡结果。实验表明，该方法在合成数据集和真实世界数据集上均实现了最优性能。代码已开源至https://github.com/Wang-ML-Lab/TSDA。