  In this work we propose a one-class self-supervised method for anomaly
segmentation in images that benefits both from a modern machine learning
approach and a more classic statistical detection theory. The method consists
of four phases. First, features are extracted using a multi-scale image
Transformer architecture. Then, these features are fed into a U-shaped
Normalizing Flow (NF) that lays the theoretical foundations for the subsequent
phases. The third phase computes a pixel-level anomaly map from the NF
embedding, and the last phase performs a segmentation based on the a contrario
framework. This multiple hypothesis testing strategy permits the derivation of
robust unsupervised detection thresholds, which are crucial in real-world
applications where an operational point is needed. The segmentation results are
evaluated using the Mean Intersection over Union (mIoU) metric, and for
assessing the generated anomaly maps we report the area under the Receiver
Operating Characteristic curve (AUROC), as well as the Area Under the
Per-Region-Overlap curve (AUPRO). Extensive experimentation in various datasets
shows that the proposed approach produces state-of-the-art results for all
metrics and all datasets, ranking first in most MVTec-AD categories, with a
mean pixel-level AUROC of 98.74%. Code and trained models are available at
https:// github.com/mtailanian/uflow.
