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Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain

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    This study introduces Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation. KATP effectively identifies transferable knowledge, preventing negative transfer and improving semantic segmentation performance.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Unsupervised domain adaptation (UDA) for semantic segmentation is crucial due to costly data annotation.
    • Existing UDA methods often fail to assess knowledge transferability, risking negative transfer from irrelevant domain knowledge.

    Purpose of the Study:

    • To develop a novel method for distinguishing transferable from untransferable knowledge in UDA.
    • To improve semantic segmentation performance by selectively transferring knowledge across domains.

    Main Methods:

    • Introduced Knowledge Aggregation-induced Transferability Perception (KATP) to quantify cross-domain knowledge transferability.
    • Proposed KATP Adaptation Network (KATPAN) with transferable appearance translation (T_A) and representation augmentation (T_R) modules.
    • KATPAN utilizes a transferability-aware information bottleneck and global category-wise prototypes for guided knowledge transfer.

    Main Results:

    • KATP effectively identifies and utilizes transferable semantic knowledge across domains.
    • The KATPAN framework demonstrates state-of-the-art performance on various benchmark datasets.
    • The proposed method shows strong results on a challenging medical imaging dataset.

    Conclusions:

    • The KATP module offers a pioneering approach to assess knowledge transferability in UDA.
    • KATPAN successfully addresses the challenge of negative transfer by adapting relevant knowledge.
    • The developed method significantly advances the field of unsupervised domain adaptation for semantic segmentation.