  Unsupervised domain adaptation is a promising way to generalize deep models
to novel domains. However, the current literature assumes that the label
distribution is domain-invariant and only aligns the feature distributions or
vice versa. In this work, we explore the more realistic task of
Class-imbalanced Domain Adaptation: How to align feature distributions across
domains while the label distributions of the two domains are also different?
Taking a practical step towards this problem, we constructed the first
benchmark with 22 cross-domain tasks from 6real-image datasets. We conducted
comprehensive experiments on 10 recent domain adaptation methods and find most
of them are very fragile in the face of coexisting feature and label
distribution shift. Towards a better solution, we further proposed a feature
and label distribution CO-ALignment (COAL) model with a novel combination of
existing ideas. COAL is empirically shown to outperform the most recent domain
adaptation methods on our benchmarks. We believe the provided benchmarks,
empirical analysis results, and the COAL baseline could stimulate and
facilitate future research towards this important problem.
