Reinforced Adaptation Network for Partial Domain Adaptation

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Reinforced Adaptation Network for Partial Domain Adaptation
Title:
Reinforced Adaptation Network for Partial Domain Adaptation
Journal Title:
IEEE Transactions on Circuits and Systems for Video Technology
Publication Date:
21 November 2022
Citation:
Wu, K., Wu, M., Chen, Z., Jin, R., Cui, W., Cao, Z., & Li, X. (2022). Reinforced Adaptation Network for Partial Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2022.3223950
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : C210112046

This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-2205
1051-8215
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