  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.

Computer Vision and Pattern Recognition