@inproceedings{3e05275d42f547509b8bf2d788bfa1c8,
title = "ANOMALY DETECTION VIA SELF-ORGANIZING MAP",
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.",
keywords = "Anomaly detection, Anomaly localization, Self-organizing map",
author = "Ning Li and Kaitao Jiang and Zhiheng Ma and Xing Wei and Xiaopeng Hong and Yihong Gong",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 28th IEEE International Conference on Image Processing, ICIP 2021 ; Conference date: 19-09-2021 Through 22-09-2021",
year = "2021",
doi = "10.1109/ICIP42928.2021.9506433",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "974--978",
booktitle = "2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings",
address = "美国",
}