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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Open Set Domain Adaptation With Soft Unknown-Class Rejection.

Yiming Xu, Lin Chen, Lixin Duan

    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces a new open set domain adaptation (OSDA) method for improved machine learning models. It effectively handles unknown target classes and aligns domains, outperforming existing approaches.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Domain Adaptation (DA) aims to train models for target domains using labeled source data and limited target data.
    • Closed Set Domain Adaptation (CSDA) assumes identical label spaces, which is impractical for real-world scenarios.
    • Open Set Domain Adaptation (OSDA) addresses scenarios with partially overlapping label spaces between source and target domains.

    Purpose of the Study:

    • To develop a novel OSDA method that addresses the limitations of conventional approaches.
    • To enable effective detection and soft rejection of unknown classes in the target domain.
    • To simultaneously align source and target domains for improved model performance.

    Main Methods:

    • Proposed a novel OSDA method incorporating soft rejection for unknown target classes.
    • Developed techniques for domain alignment between source and target data.
    • Evaluated the method on three standard datasets.

    Main Results:

    • The proposed OSDA method demonstrated superior performance compared to state-of-the-art competitors.
    • Achieved effective handling of unknown classes through soft rejection.
    • Successfully matched source and target domains.

    Conclusions:

    • The novel OSDA method offers a robust solution for real-world domain adaptation challenges.
    • Soft rejection of unknown classes improves model reliability in open set scenarios.
    • The method provides a significant advancement over existing OSDA techniques.