2021/08/17 by Vitjan Zavrtanik, Matej Kristan, Zavrtanik, Vitjan +3 · 108 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.2108.07610
Accepted to ICCV2021
openalex publication_date 2021/08/17 · arxiv created 2021/09/27 · arxiv updated 2021/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRAEM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-the-art unsupervised methods by a large margin and even delivers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.