In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted
as a discrepancy metric between the distributions of source and target domains.
However, existing MMD-based domain adaptation methods generally ignore the
changes of class prior distributions, i.e., class weight bias across domains.
This remains an open problem but ubiquitous for domain adaptation, which can be
caused by changes in sample selection criteria and application scenarios. We
show that MMD cannot account for class weight bias and results in degraded
domain adaptation performance. To address this issue, a weighted MMD model is
proposed in this paper. Specifically, we introduce class-specific auxiliary
weights into the original MMD for exploiting the class prior probability on
source and target domains, whose challenge lies in the fact that the class
label in target domain is unavailable. To account for it, our proposed weighted
MMD model is defined by introducing an auxiliary weight for each class in the
source domain, and a classification EM algorithm is suggested by alternating
between assigning the pseudo-labels, estimating auxiliary weights and updating
model parameters. Extensive experiments demonstrate the superiority of our
weighted MMD over conventional MMD for domain adaptation.

本研究提出了一种加权 MMD 方法，采用类特定辅助权重来考虑不同类别间的先验概率分布偏差，通过交替为源域中的每个类别引入辅助权重和分配伪标签以更新模型参数，解决了 MMD 忽视类别间重量偏差的问题。实验表明，加权 MMD 方法优于传统的 MMD 方法在域适应中的表现。