Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
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 l...
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MDPI AG
2025-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/4/579 |
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| author | Min Huang Zifeng Xie Bo Sun Ning Wang |
| author_facet | Min Huang Zifeng Xie Bo Sun Ning Wang |
| author_sort | Min Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-db1db44a295d4c17bc295fdb24cee5d6 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-db1db44a295d4c17bc295fdb24cee5d62025-08-20T02:03:25ZengMDPI AGMathematics2227-73902025-02-0113457910.3390/math13040579Multi-Source Unsupervised Domain Adaptation with Prototype AggregationMin Huang0Zifeng Xie1Bo Sun2Ning Wang3School of Software Engineering, South China University of Technology (SCUT), Guangzhou 510006, ChinaSchool of Software Engineering, South China University of Technology (SCUT), Guangzhou 510006, ChinaInstitute of International Services Outsourcing, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaOperation and Maintenance Center of Information and Communication, CSG EHV Power Transmission Company, Guangzhou 510663, ChinaMulti-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.https://www.mdpi.com/2227-7390/13/4/579multiple sourcesdomain adaptationprototype learningprototype aggregation |
| spellingShingle | Min Huang Zifeng Xie Bo Sun Ning Wang Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation Mathematics multiple sources domain adaptation prototype learning prototype aggregation |
| title | Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation |
| title_full | Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation |
| title_fullStr | Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation |
| title_full_unstemmed | Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation |
| title_short | Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation |
| title_sort | multi source unsupervised domain adaptation with prototype aggregation |
| topic | multiple sources domain adaptation prototype learning prototype aggregation |
| url | https://www.mdpi.com/2227-7390/13/4/579 |
| work_keys_str_mv | AT minhuang multisourceunsuperviseddomainadaptationwithprototypeaggregation AT zifengxie multisourceunsuperviseddomainadaptationwithprototypeaggregation AT bosun multisourceunsuperviseddomainadaptationwithprototypeaggregation AT ningwang multisourceunsuperviseddomainadaptationwithprototypeaggregation |