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Memory-Augmented Generative Adversarial Networks for Anomaly Detection.

Ziyi Yang, Teng Zhang, Iman Soltani Bozchalooi

    IEEE Transactions on Neural Networks and Learning Systems
    |December 28, 2021
    PubMed
    Summary

    We introduce MEMGAN, a novel memory-augmented deep learning model for robust semisupervised anomaly detection (AD). MEMGAN enhances traditional methods by using external memory units, improving the reliability of identifying abnormal data.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Traditional anomaly detection (AD) methods often struggle with distinguishing normal from abnormal data due to limitations in modeling data distributions.
    • Existing deep learning approaches require additional constraints for effective anomaly identification.

    Purpose of the Study:

    • To propose a novel memory-augmented deep learning model for semisupervised anomaly detection.
    • To enhance the robustness and reliability of anomaly detection by leveraging external memory units.

    Main Methods:

    • Development of memory augmented generative adversarial networks (MEMGAN), integrating external memory units via attentional operations.
    • Utilizing a property where encoded normal data reside within the convex hull of memory units, while abnormal data fall outside.

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    Last Updated: Oct 8, 2025

    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

    720

    Main Results:

    • MEMGAN demonstrated significant improvements over existing AD models on diverse datasets (MVTec, MNIST, CIFAR10, Arrhythmia).
    • The model exhibits a more robust and reliable anomaly detection process due to its unique latent space properties.
    • Decoded memory units in MEMGAN showed increased diversity and interpretability compared to prior memory-augmented models.

    Conclusions:

    • MEMGAN offers a superior approach to semisupervised anomaly detection by incorporating memory augmentation.
    • The model's design enhances interpretability and diversity in anomaly detection tasks.
    • This work advances the field of anomaly detection with a more effective and reliable deep learning framework.