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Papers/Anomaly Detection via Self-organizing Map

Anomaly Detection via Self-organizing Map

Ning li, Kaitao Jiang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, Yihong Gong

2021-07-21Unsupervised Anomaly DetectionAnomaly Detection
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Anomaly DetectionMVTec ADDetection AUROC97.9SOMAD
Anomaly DetectionMVTec ADSegmentation AUPRO93.3SOMAD
Anomaly DetectionMVTec ADSegmentation AUROC97.8SOMAD

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