Dual Re-Weighting Network for Multi-Source Domain Adaptation

Ziyun Cai, Tengfei Zhang, Xiao-Yuan Jing

Abstract: In this paper, we propose a novel framework called Du-al Re-weighting Multi-source Network (DRMN) to address the task of Multi-source Domain Adaptation (MSDA). Two challenges exist in MSDA: i) the domain discrepancies a-mong the multiple source domains, and ii) the domain mis-match between target and source domains. We propose du-al re- weighting mechanisms including source distribution re-weighting and sample selected re-weighting. Source distribution re-weighting mechanism can match the estimated source label distribution and the unknown target label distribution to adapt the classifier. Sample selected re-weighting mechanism can select highly confident target data as pseudo-labeled sam-ples to integrate the information from different sources, and further improve the classification performance. We find that DRMN can show competitive performance with respect to the state-of-the-art on different real-world datasets.