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Related Experiment Videos

Open-Set Domain Adaptation via Free Boundary Optimal Transport With Support Control.

Zi-Xian Huang, Chuan-Xian Ren

    IEEE Transactions on Neural Networks and Learning Systems
    |July 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces controllable partial optimal transport (POT) to enhance machine learning by incorporating class information for better domain adaptation. The new method effectively distinguishes between known and unknown classes in open set domain adaptation.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Data Science

    Background:

    • Optimal transport (OT) excels at measuring distributional differences but struggles with label-related information.
    • Standard OT underperforms in real-world scenarios and open set domain adaptation (OSDA) due to data structure limitations.
    • Discriminating between known and unknown classes is crucial for OSDA, necessitating class-aware transport mechanisms.

    Purpose of the Study:

    • To develop a controllable transport mechanism that incorporates class information for improved learning of complex data structures.
    • To address the limitations of traditional optimal transport in scenarios requiring class discrimination, particularly in open set domain adaptation.

    Main Methods:

    • Propose controllable partial optimal transport (POT) using a free boundary formulation to control transport plan supports via an analytic parameter.
    • Provide theoretical justification for support control enabling class-conditional distribution alignment and establish equivalence between free boundary and conditional OT.
    • Develop a two-point metric module to create a gap between interclass and intraclass transport, ensuring empirical validity.

    Main Results:

    • The proposed support control mechanism facilitates class-conditional distribution alignment.
    • The method effectively quantifies interclass and intraclass transport behaviors, reducing transport support learning to a binary decision problem.
    • Extensive experiments validate theoretical findings and demonstrate the proposed model's effectiveness in OSDA.

    Conclusions:

    • Controllable partial optimal transport offers a novel approach to enhance domain adaptation by integrating class-specific information.
    • The method provides a robust framework for distinguishing between known and unknown classes, crucial for real-world machine learning applications.
    • The developed two-point metric module significantly improves the empirical performance and validity of the proposed class-aware transport mechanism.