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arXiv · 2107.09903

Anomaly Detection via Self-organizing Map

Ning Li·Kaitao Jiang·Zhiheng Ma·Xing Wei·Xiaopeng Hong·Yihong Gong
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Abstract

Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization ability. Recent methods based on supervised deep learning are more powerful but require large-scale annotated datasets for training. In practice, abnormal products are rare thus it is very difficult to train a deep model in a fully supervised way. In this paper, we propose a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM). Our method, Self-organizing Map for Anomaly Detection (SOMAD) maintains normal characteristics by using topological memory based on multi-scale features. SOMAD achieves state-of the-art performance on unsupervised anomaly detection and localization on the MVTec dataset.

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Ning Li, Kaitao Jiang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, Yihong Gong. 2021-07-21. Anomaly Detection via Self-organizing Map. https://arxiv.org/abs/2107.09903

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