Reinforced Reweighting for Self-supervised Partial Domain Adaptation

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Reinforced Reweighting for Self-supervised Partial Domain Adaptation
Title:
Reinforced Reweighting for Self-supervised Partial Domain Adaptation
Journal Title:
IEEE Transactions on Artificial Intelligence
Keywords:
Publication Date:
07 May 2024
Citation:
Wu, K., Chen, S., Wu, M., Xiang, S., Jin, R., Xu, Y., Li, X., & Chen, Z. (2024). Reinforced Reweighting for Self-supervised Partial Domain Adaptation. IEEE Transactions on Artificial Intelligence, 1–10. https://doi.org/10.1109/tai.2024.3397288
Abstract:
Domain adaptation enables the reduction of distribution differences across domains, allowing for effective knowledge transfer from one domain to a different domain. In recent years, partial domain adaptation (PDA) has attracted growing interest due to its focus on a more realistic scenario, where the target label space is a subset of the source label space. As the source and target domains do not possess the same label space in the PDA setting, it is challenging but crucial to mitigate the domain gap without incurring negative transfer. In this paper, we propose a Reinforced Reweighting united with Self-supervised Adaptation (R2SA) method to address the challenges in PDA by leveraging the merits of deep reinforcement learning (DRL) and self-supervised learning (SSL) simultaneously in a cooperative way. Reinforced reweighting aims to learn a source reweighting policy automatically based on information provided by the PDA model, while self-supervised adaptation aims to boost the adaptability of the PDA model through an additional self-supervised objective on the target domain. Extensive experiments on several cross-domain benchmarks demonstrate that our method achieves state-of-the-art results, with larger performance gains on more challenging tasks.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation of Singapore - AME Young Individual Research Grant
Grant Reference no. : A2084c0167
Description:
© 2024 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:
2691-4581
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