Multi‐source to multi‐target domain adaptation method based on similarity measurement

Abstract

Abstract Most existing domain adaption methods solve the problem of data distribution similarity in single‐source to single‐target domain or multi‐source to single‐target domain adaption, but the more realistic multi‐source to multi‐target domain transfer scenarios are ignored. At the same time, less attention is paid to the similarity of task distribution among domains and the corresponding relationship between coarse‐grained and fine‐grained, which leads to the problem of low classification accuracy. In this paper, a multi‐source to multi‐target domain adaption (MSMTDA) method based on similarity measurement is proposed. Firstly, a cross‐domain similarity measurement mechanism based on the idea of data distribution similarity and task distribution similarity is set up to judge and optimize the distribution difference among source domains and target domains. Secondly, global‐local‐bidirectional alignment is used to solve the problem of poor transfer performance in the existing distributed alignment. Then, the cross‐domain mutual learning strategy is proposed, which reduces classifier discrepancy caused by decision boundaries and distribution difference in multiple domains. Finally, the proposed method is verified on three benchmark datasets in image classification. The experimental results show that the proposed method performs better in both classification accuracy and training time when compared with the conventional method.

Keywords

• cross‐domain mutual learning strategy
• distributed alignment
• domain adaptation
• image classification
• similarity measurement