Abstract
Unsupervised domain adaptation methods utilize feature re-presentations of instances in the source and target domains to eliminate domain shifts. It is worth noting that the instance features are closely related to the entire distribution of the domain, and the current information after adaptation in the execution of the domain adaptation task is closely related to the original features. Common methods are based on only one of these pieces of information and do not make sufficient use of them. We develop the Self-Reinforcing Feedback Domain Adaptation Channel (SRFC). Pioneeringly, on the feature representation of the network, SRFC fuses global and instance information simultaneously, and utilizes the past history and current information in domain adaptation, so that the information can be effectively enhanced to better complete the domain adaptation. Through the designed self-reinforcing feedback mechanism, SRFC skillfully integrates multi-level information in a robust way in the process of domain adaptation, and actively enhances the availability and comprehensive value of features in domain adaptation with manageable continuous feedback. Experiments on benchmark datasets verify the advantages of SRFC fusion information for instance feature enhancement and domain adaptation. The modular Self-Reinforcing Feedback Domain Adaptation Channel has scalability and R &D potential, and we hope that it can be extended to more domain adaptation networks using enhanced instance representations to better accomplish different tasks.
This work was funded by Haihe Laboratory in Tianjin, Grants No. 22HHXCJC00007.
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Jia, Y., Zhang, X., Lan, L., Luo, Z. (2023). Self-Reinforcing Feedback Domain Adaptation Channel. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_12
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