A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning (2024)

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  • Tuo Xu VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

    VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

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  • Bing Han VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

    VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

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  • Jie Li VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

    VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China

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  • Yuefan Du MCI Laboratory, School of Aerospace Science and Technology, Xidian University, Xian, Shannxi, China

    MCI Laboratory, School of Aerospace Science and Technology, Xidian University, Xian, Shannxi, China

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Image and Vision ComputingVolume 141Issue CJan 2024https://doi.org/10.1016/j.imavis.2023.104887

Published:17 April 2024Publication History

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Image and Vision Computing

Volume 141, Issue C

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A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning (1)

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Abstract

Abstract

Domain adaptation has found extensive applications in diverse fields, including transfer learning, deep learning, and image processing, to effectively address the challenge of mismatched data distributions. Nevertheless, persistent challenges such as limited transfer data and the occurrence of negative transfer continue to exist. These issues not only increase the complexity of model learning but also affect the accuracy of the transfer results. To tackle these challenges, this paper introduces a novel approach called the Related Weighted Subdomain Adaptive Network (RWSAN). This network constructs related subdomain clusters in the source domain to encompass a wider spectrum of associated information. It aims to align target domain subdomains with the related subdomain clusters in the source domain as closely as possible, thus increasing the number of transferable samples from the source domain and solving insufficient transferable samples in some adaptations. To address the issue of negative transfer arising from irrelevant subdomains in the source domain during the transfer process, a related weighted maximum mean discrepancy method is introduced. This method optimizes transfer weights, minimizes the feature distribution disparity between the target domain and the source domain, and mitigates the negative impact of irrelevant subdomains on the target domain, thereby enhancing the accuracy of domain adaptation during transfer. To evaluate the proposed RWSAN method, comprehensive performance tests were conducted using widely used red-green-blue (RGB) and hyperspectral image (HIS) image datasets. The results demonstrate that the RWSAN method effectively resolves insufficient transfer data and negative transfer, proving its high reliability. Additionally, it exhibits excellent performance in error analysis, network convergence analysis, data visualization, and distribution difference.

Highlights

New Related Weighted Subdomain Adaptive Network (RWSAN) proposed.

RWSAN constructs related subdomain clusters in source domain.

RWSAN addresses issue of limited transfer data.

Method proposed to minimize feature distribution disparity.

Mitigates negative transfer from irrelevant subdomains.

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      A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning (48)

      Image and Vision Computing Volume 141, Issue C

      Jan 2024

      138 pages

      ISSN:0262-8856

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      Elsevier B.V.

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          Publication History

          • Published: 17 April 2024

          Author Tags

          • Domain adaptation
          • Correlation subdomain adaptation
          • Maximum mean difference
          • Correlation transfer learning
          • Deep learning

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