Reinforced Adaptation Network for Partial Domain Adaptation

被引:6
|
作者
Wu, Keyu [1 ]
Wu, Min [1 ]
Chen, Zhenghua [2 ]
Jin, Ruibing [1 ]
Cui, Wei [1 ]
Cao, Zhiguang [1 ]
Li, Xiaoli [2 ]
机构
[1] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore
[2] ASTAR, Inst Infocomm Res, 138632, Singapore, Singapore
关键词
Adaptation models; Reinforcement learning; Knowledge transfer; Training; Data models; Task analysis; Minimization; Deep reinforcement learning; partial domain adaptation; domain adaptation; transfer learning;
D O I
10.1109/TCSVT.2022.3223950
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:2370 / 2380
页数:11
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