Category-aware and reweighting-based multi-source domain adaptation algorithm

Home > Archive>Volume 47, Issue 17, 2024 >80-88

Category-aware and reweighting-based multi-source domain adaptation algorithm
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                        Tan MianTan Mian
1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China； 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
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Li ZhilingLi Zhiling
1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China； 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
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Chen WangChen Wang
1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China； 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
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Zeng TaotaoZeng Taotao
1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China； 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
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Feng FujianFeng Fujian
1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China； 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
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Affiliation:1.School of Mathematical Sciences and Information Engineering，Guizhou Minzu University，Guiyang 550025,China; 2.Guizhou Minzu University，Key Laboratory of Pattern Recognition and Intelligent System，Guiyang 550025,China
Clc Number:TP181;TN911.7
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Abstract:

Multi-source domain adaptation is an important branch of transfer learning. Category shift, a prominent challenge in this field, stems from the mismatch between category distributions in the source and target domains. To address this problem, a category-aware and reweighting-based multi-source domain adaptation algorithm is proposed. The algorithm enhances positive transfer between similar categories through a category-aware strategy and introduces a reweighting moment matching strategy to reduce distribution differences at various levels. Additionally, adaptive weights are constructed using pseudo-labels to effectively mitigate the impact of category shift. Experimental results on the Digits-Five and Office-Caltech10 datasets show that the proposed algorithm achieves classification accuracies of 94.11% and 97.18%, respectively. These results indicate that the proposed algorithm significantly improves accuracy in scenarios with category shift compared to current typical multi-source domain adaptation algorithms.

Key words:transfer learning;multi-source domain adaptation;category awareness;moment matching;adaptive weighting

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Online: December 11,2024
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