A Novel Multi-Source Domain Adaptation Method with Dempster–Shafer Evidence Theory for Cross-Domain Classification

Abstract

In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much information, the processing of domain shifts becomes more challenging, especially in learning a common domain-invariant representation for all domains. Moreover, it is counter-intuitive to treat multiple source domains equally as most existing MDA algorithms do. Therefore, the domain-specific distribution for each source–target domain pair is aligned, respectively. Nevertheless, it is hard to combine adaptation outputs from different domain-specific classifiers effectively, because of ambiguity on the category boundary. Subjective Logic (SL) is introduced to measure the uncertainty (credibility) of each domain-specific classifier, so that MDA could be bridged with DST. Due to the advantage of information fusion, Dempster–Shafer evidence Theory (DST) is utilized to reduce the category boundary ambiguity and output reasonable decisions by combining adaptation outputs based on uncertainty. Finally, extensive comparative experiments on three popular benchmark datasets for cross-domain image classification are conducted to evaluate the performance of the proposed method via various aspects.