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Updated: Sep 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo.

Jiangbo Pei, Ruizhe Li, Aidong Men

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 31, 2025
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    Summary
    This summary is machine-generated.

    This study introduces Zoo-MSFDA, a flexible domain adaptation setting with diverse source models. A novel method, SUTE, enables effective model selection and ensemble adaptation for improved performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Conventional Multi-Source Free Domain Adaptation (MSFDA) relies on uniform source models.
    • This limits adaptability when source domains offer diverse model architectures.

    Purpose of the Study:

    • To introduce Zoo-MSFDA, a generalized setting accommodating multiple diverse source models per domain.
    • To address the challenge of suboptimal models in Zoo-MSFDA.
    • To propose a novel method for transferability estimation.

    Main Methods:

    • Theoretical analysis of model selection using transferability and diversity principles.
    • Development of Source-Free Unsupervised Transferability Estimation (SUTE) for cross-architecture transferability assessment.
    • Introduction of a Selection, Ensemble, and Adaptation (SEA) framework.

    Main Results:

    • The SEA framework significantly enhances adaptation performance in 2D image classification.
    • SUTE demonstrates state-of-the-art performance in transferability estimation.
    • Zoo-MSFDA setting proves effective with diverse source models.

    Conclusions:

    • Zoo-MSFDA offers a more general and effective approach to multi-source domain adaptation.
    • SUTE provides a robust solution for evaluating model transferability without source data or target labels.
    • The SEA framework successfully leverages diverse source models for improved adaptation.