  Recent advances in unsupervised domain adaptation (UDA) show that
transferable prototypical learning presents a powerful means for class
conditional alignment, which encourages the closeness of cross-domain class
centroids. However, the cross-domain inner-class compactness and the underlying
fine-grained subtype structure remained largely underexplored. In this work, we
propose to adaptively carry out the fine-grained subtype-aware alignment by
explicitly enforcing the class-wise separation and subtype-wise compactness
with intermediate pseudo labels. Our key insight is that the unlabeled subtypes
of a class can be divergent to one another with different conditional and label
shifts, while inheriting the local proximity within a subtype. The cases of
with or without the prior information on subtype numbers are investigated to
discover the underlying subtype structure in an online fashion. The proposed
subtype-aware dynamic UDA achieves promising results on medical diagnosis
tasks.
