MVTec-AD–A comprehensive real-world dataset for unsupervised anomaly detection

Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods often struggle with limited generalization capabilities due to their focus on a single category, and can fail when encountering variations in product. Recent feature reconstruction methods, as representatives in one-model–all-categories schemes, face challenges including reconstructing anomalous samples and blurry reconstructions.