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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Bringing Equity to Classification: Domain Generalization for Domain-Linked Classes.

Kimathi Kaai, Saad Hossain, Sirisha Rambhatla

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 11, 2025
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    Domain generalization struggles with domain-linked classes. We introduce FOND, a novel method using contrastive learning to improve knowledge transfer for these challenging classes, achieving state-of-the-art results.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Domain generalization (DG) aims to transfer knowledge from source to unseen target domains.
    • Standard DG assumes classes are present across multiple domains to avoid spurious correlations.
    • Data scarcity often leads to domain-linked classes, hindering generalization.

    Purpose of the Study:

    • Introduce the challenging domain-linked DG task.
    • Develop a methodology for learning domain-invariant representations for domain-linked classes.
    • Address limitations of existing DG approaches when facing domain-specific classes.

    Main Methods:

    • Propose FOND (Fairness-inspired and cONtrastive learning objective for Domain-linked DG).
    • Utilize contrastive learning to bridge domain-shared and domain-linked classes.
    • Develop a fairness-inspired objective to mitigate negative transfer.

    Main Results:

    • FOND achieves state-of-the-art performance on the domain-linked DG task.
    • Demonstrates significant improvements for domain-linked classes.
    • Shows minimal performance trade-offs on domain-shared classes.

    Conclusions:

    • The proposed FOND method effectively addresses the domain-linked DG challenge.
    • Highlights the importance of handling domain-specific classes for robust generalization.
    • Provides theoretical insights and practical guidance for domain-linked class generalizability.