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Unsupervised anomaly detection and localization is crucial to the practicalapplication when collecting and labeling sufficient anomaly data is infeasible.Most existing representation-based approaches extract normal image featureswith a deep convolutional neural network and characterize the correspondingdistribution through non-parametric distribution estimation methods. Theanomaly score is calculated by measuring the distance between the feature ofthe test image and the estimated distribution. However, current methods can noteffectively map image features to a tractable base distribution and ignore therelationship between local and global features which are important to identifyanomalies. To this end, we propose FastFlow implemented with 2D normalizingflows and use it as the probability distribution estimator. Our FastFlow can beused as a plug-in module with arbitrary deep feature extractors such as ResNetand vision transformer for unsupervised anomaly detection and localization. Intraining phase, FastFlow learns to transform the input visual feature into atractable distribution and obtains the likelihood to recognize anomalies ininference phase. Extensive experimental results on the MVTec AD dataset showthat FastFlow surpasses previous state-of-the-art methods in terms of accuracyand inference efficiency with various backbone networks. Our approach achieves99.4% AUC in anomaly detection with high inference efficiency.

FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows