DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

🤖 Plain-English Summary

Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. On the challenging MVTec anomaly detection dataset, DRÆM outperforms the current advanced unsupervised methods by a large margin and even de-livers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.

🔑 Key Findings

• 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 (DRÆM).

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