  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.
