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Guided Discrimination and Correlation Subspace Learning for Domain Adaptation.

Yuwu Lu, Wai Keung Wong, Biqing Zeng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 5, 2023

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces Guided Discrimination and Correlation Subspace Learning (GDCSL) for domain adaptation in image classification. GDCSL enhances feature transfer by ensuring domain invariance, discriminative power, and feature correlation, while minimizing negative transfer.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Domain adaptation is crucial for transfer learning, aiming to apply knowledge from source to target domains.
    • Existing methods often overlook feature discriminability, correlation, and the risk of negative transfer.

    Purpose of the Study:

    • To propose a novel domain adaptation method, Guided Discrimination and Correlation Subspace Learning (GDCSL), for cross-domain image classification.
    • To address limitations in existing methods by focusing on domain-invariant, category-discriminative, and correlated features, while avoiding negative transfer.

    Main Methods:

    • GDCSL minimizes intraclass scatter and maximizes interclass distance for discriminative features.
    • A novel correlation term extracts maximally correlated features between domains.
  • Sample reweighting and a label selection scheme mitigate negative transfer and improve pseudo-label accuracy in the semi-supervised extension (Semi-GDCSL).

    • Main Results:

    • GDCSL effectively learns domain-invariant, category-discriminative, and correlated features.
    • The method demonstrates superior performance compared to state-of-the-art domain adaptation techniques on benchmark datasets.
    • The semi-supervised extension (Semi-GDCSL) further enhances performance through improved pseudo-labeling.

    Conclusions:

    • GDCSL offers a robust framework for cross-domain image classification by addressing key overlooked factors in domain adaptation.
    • The proposed method significantly improves transfer learning efficiency and accuracy.
    • GDCSL and its semi-supervised variant represent advancements in domain adaptation research.