centroIDA: Cross-Domain Class Discrepancy Minimization Based on
  Accumulative Class-Centroids for Imbalanced Domain Adaptation
Xiaona Sun Zhenyu Wu Yichen Liu Saier Hu Zhiqiang Zhan Yang Ji
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift
problem by minimizing the distribution discrepancy between the source and
target domains, assuming that the label distribution is invariant across
domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate
and long-tailed label shifts both exist across domains. To tackle the IDA
problem, some current research focus on minimizing the distribution
discrepancies of each corresponding class between source and target domains.
Such methods rely much on the reliable pseudo labels' selection and the feature
distributions estimation for target domain, and the minority classes with
limited numbers makes the estimations more uncertainty, which influences the
model's performance. In this paper, we propose a cross-domain class discrepancy
minimization method based on accumulative class-centroids for IDA (centroIDA).
Firstly, class-based re-sampling strategy is used to obtain an unbiased
classifier on source domain. Secondly, the accumulative class-centroids
alignment loss is proposed for iterative class-centroids alignment across
domains. Finally, class-wise feature alignment loss is used to optimize the
feature representation for a robust classification boundary. A series of
experiments have proved that our method outperforms other SOTA methods on IDA
problem, especially with the increasing degree of label shift.