Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation

Min Huang (), Zifeng Xie, Bo Sun and Ning Wang
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Min Huang: School of Software Engineering, South China University of Technology (SCUT), Guangzhou 510006, China
Zifeng Xie: School of Software Engineering, South China University of Technology (SCUT), Guangzhou 510006, China
Bo Sun: Institute of International Services Outsourcing, Guangdong University of Foreign Studies, Guangzhou 510006, China
Ning Wang: Operation and Maintenance Center of Information and Communication, CSG EHV Power Transmission Company, Guangzhou 510663, China

Mathematics, 2025, vol. 13, issue 4, 1-18

Abstract: Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments.

Keywords: multiple sources; domain adaptation; prototype learning; prototype aggregation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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