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.

本文提出了一种基于判别模型的重构异常嵌入模型（DRAEM），用于解决视觉表面异常检测问题，该方法无需手工后处理即可实现异常区域定位。实验结果表明 DRAEM 在 MVTec 异常检测数据集上比当前最先进的无监督方法优异得多，甚至在 DAGM 表面缺陷检测数据集上也可以接近全监督方法的检测性能，并且比全监督方法在定位准确度上表现更好。

DRAEM—面部异常检测的鉴别训练重构嵌入