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Unsupervised anomaly detection with localization has many practicalapplications when labeling is infeasible and, moreover, when anomaly examplesare completely missing in the train data. While recently proposed models forsuch data setup achieve high accuracy metrics, their complexity is a limitingfactor for real-time processing. In this paper, we propose a real-time modeland analytically derive its relationship to prior methods. Our CFLOW-AD modelis based on a conditional normalizing flow framework adopted for anomalydetection with localization. In particular, CFLOW-AD consists of adiscriminatively pretrained encoder followed by a multi-scale generativedecoders where the latter explicitly estimate likelihood of the encodedfeatures. Our approach results in a computationally and memory-efficient model:CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-artwith the same input setting. Our experiments on the MVTec dataset show thatCFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-sourceour code with fully reproducible experiments.

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows