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    Area of Science:

    • Sensor Systems and Measurement Science
    • Machine Learning and Artificial Intelligence

    Background:

    • Domain adaptation is crucial when training and test data distributions differ.
    • Instrumental variation and time-varying drift represent significant challenges in sensor and measurement data, causing discrete and continuous distributional shifts.
    • Existing domain adaptation methods may not uniformly handle these diverse distributional changes.

    Purpose of the Study:

    • To introduce Maximum Independence Domain Adaptation (MIDA) and its semi-supervised variant to address distributional shifts in sensor and measurement data.
    • To develop a method that effectively reduces inter-domain discrepancies caused by instrumental variation and drift.
    • To enhance the practicability and application scope of domain adaptation algorithms for real-world sensor systems.

    Main Methods:

    • Defined domain features to capture background information (e.g., device, acquisition time).
    • Developed MIDA to learn a subspace maximally independent of domain features, minimizing distributional differences.
    • Implemented a feature augmentation strategy to project samples based on their background, improving adaptation.

    Main Results:

    • Demonstrated the effectiveness of MIDA and semi-supervised MIDA on synthetic and four real-world datasets (sensors, measurement, computer vision).
    • Showcased the algorithms' flexibility and speed in handling discrete and continuous distributional changes.
    • Achieved significant enhancements in the practicability of sensor systems.

    Conclusions:

    • MIDA provides a robust solution for domain adaptation problems characterized by instrumental variation and time-varying drift.
    • The proposed algorithms uniformly handle diverse distributional changes, extending the applicability of existing domain adaptation techniques.
    • MIDA significantly improves the performance and reliability of sensor and measurement systems in real-world applications.