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.
@article{arxiv.2107.09903,
title = {Anomaly Detection via Self-organizing Map},
author = {Ning Li and Kaitao Jiang and Zhiheng Ma and Xing Wei and Xiaopeng Hong and Yihong Gong},
journal= {arXiv preprint arXiv:2107.09903},
year = {2021}
}
Comments
International Conference on Image Processing(ICIP), 2021