Deep Reinforcement Learning Boosted Partial Domain Adaptation

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Deep Reinforcement Learning Boosted Partial Domain Adaptation
Title:
Deep Reinforcement Learning Boosted Partial Domain Adaptation
Other Titles:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Publication Date:
21 August 2021
Citation:
Wu, K., Wu, M., Yang, J., Chen, Z., Li, Z., & Li, X. (2021). Deep Reinforcement Learning Boosted Partial Domain Adaptation. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2021/439
Abstract:
Domain adaptation is critical for learning transferable features that effectively reduce the distribution difference among domains. In the era of big data, the availability of large-scale labeled datasets motivates partial domain adaptation (PDA) which deals with adaptation from large source domains to small target domains with less number of classes. In the PDA setting, it is crucial to transfer relevant source samples and eliminate irrelevant ones to mitigate negative transfer. In this paper, we propose a deep reinforcement learning based source data selector for PDA, which is capable of eliminating less relevant source samples automatically to boost existing adaptation methods. It determines to either keep or discard the source instances based on their feature representations so that more effective knowledge transfer across domains can be achieved via filtering out irrelevant samples. As a general module, the proposed DRL-based data selector can be integrated into any existing domain adaptation or partial domain adaptation models. Extensive experiments on several benchmark datasets demonstrate the superiority of the proposed DRL-based data selector which leads to state-of-the-art performance for various PDA tasks.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funds - Learning with Less Data
Grant Reference no. : A20H6b0151

This research / project is supported by the Agency for Science, Technology and Research - Career Development Award - Contrastive learning for time series domain adaptation
Grant Reference no. : C210112046
Description:
ISSN:
NA