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Updated: Dec 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity.

Takashi Matsubara, Kazuki Sato, Kenta Hama

    IEEE Transactions on Cybernetics
    |October 29, 2020
    PubMed
    Summary

    A new unregularized score for deep generative models (DGMs) improves anomaly detection. This method overcomes limitations of standard probabilistic models, enabling accurate identification of subtle defects in complex image datasets.

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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    875

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Probabilistic models struggle with anomaly detection in heterogeneous image datasets.
    • Complex object shapes can be incorrectly penalized by standard models, hindering defect identification.
    • Small anomalies like scratches or grime are often missed by conventional methods.

    Purpose of the Study:

    • To propose an unregularized score for deep generative models (DGMs) to enhance anomaly detection.
    • To address the limitations of existing probabilistic models in handling sample complexity.
    • To improve the accuracy and selectivity of defect detection in image datasets.

    Main Methods:

    • Developed an unregularized anomaly scoring method for deep generative models.
    • Investigated the influence of regularization terms on anomaly scores based on sample complexity.
    • Evaluated the proposed score on diverse datasets, including toy, manufacturing, and medical data.

    Main Results:

    • The unregularized score demonstrates robustness against variations in sample complexity.
    • The proposed method effectively detects anomalies, including small defects like scratches and grime.
    • Empirical results confirm improved anomaly detection performance compared to standard approaches.

    Conclusions:

    • The unregularized score offers a significant advancement for anomaly detection in image analysis.
    • This approach enhances the reliability of automated defect detection systems.
    • The method shows promise for applications in manufacturing quality control and medical imaging.