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Updated: Oct 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Contrastive Learning Assisted-Alignment for Partial Domain Adaptation.

Cuie Yang, Yiu-Ming Cheung, Jinliang Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |February 7, 2022
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    Summary
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    This study introduces contrastive learning-assisted alignment (CLA) for unsupervised partial domain adaptation (PDA). CLA effectively aligns distributions and reweights source data to improve model performance in target domains with fewer classes.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Unsupervised partial domain adaptation (PDA) presents challenges in leveraging shared classes for positive transfer while mitigating negative transfer from irrelevant source classes.
    • Existing adversarial domain adaptation methods often overlook the preservation of class-discriminative representations.

    Purpose of the Study:

    • To propose a novel approach for unsupervised partial domain adaptation (PDA) that addresses the limitations of existing methods.
    • To enhance domain alignment and reduce negative transfer by reweighting source instances.

    Main Methods:

    • A contrastive learning-assisted alignment (CLA) approach is introduced, integrating contrastive learning with adversarial domain adaptation.
    • A contrastive learning-assisted conditional alignment (CLCA) strategy is employed to discover class-discriminative information and align domain distributions.
    • A new reweighting scheme is developed to improve the estimation of source instance weights by utilizing information from both source and target domains.

    Main Results:

    • The proposed CLA method demonstrates superior performance compared to existing state-of-the-art PDA techniques.
    • Empirical results on benchmark datasets validate the effectiveness of the CLA approach in unsupervised partial domain adaptation.

    Conclusions:

    • The CLA approach effectively addresses the challenges of unsupervised partial domain adaptation by jointly aligning distributions and reweighting source instances.
    • The method successfully leverages contrastive learning to preserve class discriminative information, leading to improved adaptation performance.