A Two-Way alignment approach for unsupervised multi-Source domain adaptation

•We introduce a two-way alignment framework for unsupervised multisource domain adaptation task. We first align the target and multiple source domains on domain-level by an adversarial learning process, and then reduce the domain gap on category-level by minimizing the distance between the category prototypes and target instances with a minimax entropy loss.•We propose an instance weighting strategy to mitigate the impact of instance variations inside each domain.•Experimental results on several datasets demonstrate the superiority of the proposed algorithm to several state-of-the-art approaches.