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.

本文提出了一个更接近实际情况的机器学习问题：“不同领域的特征分布不同，且标签分布不均衡，如何将它们对齐？” 并构建了第一个包括 22 个跨域任务的基准测试数据集，并使用 COAL 模型进行了广泛的实验，发现大部分最新的领域自适应方法在同时具有特征和标签分布变化方面非常脆弱。COAL 模型能够有效地实现特征和标签的对齐，对于该问题的未来研究能提供更好的基准测试数据集。