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In this work we propose a non-contrastive method for anomaly detection andsegmentation in images, that benefits both from a modern machine learningapproach and a more classic statistical detection theory. The method consistsof three phases. First, features are extracted using a multi-scale imageTransformer architecture. Then, these features are fed into a U-shapedNormalizing Flow that lays the theoretical foundations for the last phase,which computes a pixel-level anomaly map, and performs a segmentation based onthe a contrario framework. This multiple-hypothesis testing strategy permits toderive robust automatic detection thresholds, which are crucial in manyreal-world applications, where an operational point is needed. The segmentationresults are evaluated using the Intersection over Union (IoU) metric; and forassessing the generated anomaly maps we report the area under the ReceiverOperating Characteristic curve (AUROC), and the area under theper-region-overlap curve (AUPRO). Extensive experimentation in various datasetsshows that the proposed approach produces state-of-the-art results for allmetrics and all datasets, ranking first in most MvTec-AD categories, with amean pixel-level AUROC of 98.74%. Code and trained models are available athttps:// github.com/mtailanian/uflow.

U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold