Anomaly Detection Via Self-Organizing Map

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

202135 citationsDOI

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.

Topics & Concepts

Anomaly detectionComputer scienceArtificial intelligenceGeneralizationUnsupervised learningSelf-organizing mapAnomaly (physics)Scale (ratio)Pattern recognition (psychology)Deep learningKey (lock)Machine learningArtificial neural networkMathematicsGeographyCartographyCondensed matter physicsComputer securityMathematical analysisPhysicsAnomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect DetectionDigital Media Forensic Detection