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The state-of-the-art in discriminative unsupervised surface anomaly detectionrelies on external datasets for synthesizing anomaly-augmented training images.Such approaches are prone to failure on near-in-distribution anomalies sincethese are difficult to be synthesized realistically due to their similarity toanomaly-free regions. We propose an architecture based on quantized featurespace representation with dual decoders, DSR, that avoids the image-levelanomaly synthesis requirement. Without making any assumptions about the visualproperties of anomalies, DSR generates the anomalies at the feature level bysampling the learned quantized feature space, which allows a controlledgeneration of near-in-distribution anomalies. DSR achieves state-of-the-artresults on the KSDD2 and MVTec anomaly detection datasets. The experiments onthe challenging real-world KSDD2 dataset show that DSR significantlyoutperforms other unsupervised surface anomaly detection methods, improving theprevious top-performing methods by 10% AP in anomaly detection and 35% AP inanomaly localization.

DSR -- A dual subspace re-projection network for surface anomaly detection