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    Knowledge transfer from labeled to unlabeled data is crucial. This study introduces open set domain adaptation to handle unknown categories in target datasets, achieving state-of-the-art results in image and action recognition.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Annotating large datasets is costly, necessitating knowledge transfer to unlabeled data.
    • Real-world data often exhibits domain shift, where target data differs from source datasets.
    • Existing domain adaptation methods assume known target categories, which is often not the case.

    Purpose of the Study:

    • To propose a novel approach for open set domain adaptation.
    • To address scenarios where target domains contain categories absent in the source domain.
    • To develop a versatile method applicable to various domain adaptation settings.

    Main Methods:

    • Developed an approach for open set domain adaptation.
    • The method handles discrepancies in category sets between source and target domains.
    • Evaluated on diverse datasets for image classification and action recognition.

    Main Results:

    • Achieved state-of-the-art performance on multiple benchmark datasets.
    • Demonstrated effectiveness in image classification and action recognition tasks.
    • The approach successfully adapts knowledge across domains with unknown target categories.

    Conclusions:

    • The proposed method offers a robust solution for open set domain adaptation.
    • It provides a versatile tool for unsupervised, semi-supervised, open set, and closed set domain adaptation.
    • This research advances the capability to leverage existing labeled data for diverse, real-world applications.