Reinforced Adaptation Network for Partial Domain Adaptation

Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature representations. By combining reinforcement learning and domain adaptation techniques, the proposed network alleviates negative transfer by automatically filtering out less relevant source data and promotes positive transfer by minimizing the distribution discrepancy across domains. Experiments on three benchmark datasets demonstrate that RAN consistently outperforms seventeen existing state-of-the-art methods by a large margin.

Automated summary

Domain adaptation transfers knowledge from label-rich source domains to label-scarce target domains for generalized learning in new environments. Partial domain adaptation (PDA) extends this concept by considering scenarios where the target label space is a subset of the source label space. This paper proposes a Reinforced Adaptation Network (RAN) that combines deep reinforcement learning with domain adaptation techniques to address the challenging PDA problem. Experimental results show that RAN significantly outperforms existing state-of-the-art methods on three benchmark datasets.