Cross Domain Mean Approximation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data. It is a key step for UDA to minimize the cross-domain distribution divergence. In this paper, we firstly propose a novel discrepancy metric, referred to as Cross Domain Mean Approximation (CDMA) discrepancy, to evaluate the distribution differences between source and target domains, which calculate the sum of the squares of the distances from the source and target domains to the mean of the other domain. Secondly, Joint Distribution Adaptation based on Cross Domain Mean Approximation (JDA-CDMA) is developed on the basis of CDMA to extract shared feature and simultaneously reduce the marginal and conditional distribution discrepancy between domains during the label refinement process. Thirdly, we construct a classifier utilizing CDMA metric and neighbor information. Finally, the proposed feature extraction approach and classifier are combined to realize transfer learning. Results from extensive experiments on five visual benchmarks including object, face, and digit images, show the proposed methods outperform the state-of-the-art unsupervised domain adaptation.