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Related Experiment Videos

Improving anomaly detection with foundation-model synthesis and wavelet-domain attention.

Wensheng Wu1, Zheming Lu1, Ziqian Lu2

  • 1School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 24, 2026
PubMed
Summary

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This study introduces a novel anomaly synthesis pipeline (FMAS) and a Wavelet Domain Attention Module (WDAM) to improve industrial anomaly detection. These methods generate realistic anomalies and enhance feature extraction, boosting detection accuracy efficiently.

Area of Science:

  • Industrial anomaly detection
  • Machine Learning
  • Computer Vision

Background:

  • Industrial anomaly detection is challenged by limited anomalous data and complex anomaly types.
  • Existing methods often require extensive training data or struggle with diverse anomalies.

Purpose of the Study:

  • To develop a foundation model-based anomaly synthesis pipeline (FMAS) for generating realistic anomalous samples.
  • To introduce a Wavelet Domain Attention Module (WDAM) for enhanced anomaly feature extraction in the frequency domain.
  • To improve the sensitivity and efficiency of industrial anomaly detection systems.

Main Methods:

  • FMAS generates synthetic anomalies without fine-tuning or class-specific training.
  • WDAM utilizes adaptive sub-band processing in the wavelet domain to focus on anomaly-related features.
Keywords:
Anomaly detectionAnomaly generationWavelet domain attention

Related Experiment Videos

  • The proposed methods were evaluated on MVTec AD and VisA datasets.

    • Main Results:

    • The FMAS pipeline successfully generates highly realistic anomalous samples.
    • WDAM significantly enhances anomaly feature extraction by exploiting frequency-domain characteristics.
    • The combined approach demonstrates substantial performance gains over existing baselines.
    • WDAM functions as an effective plug-and-play module.

    Conclusions:

    • The proposed FMAS and WDAM offer a powerful solution for industrial anomaly detection, particularly when anomalous data is scarce.
    • WDAM improves detection sensitivity and maintains computational efficiency.
    • These advancements contribute to more robust and effective automated inspection systems.