Normalizing Flows for Real-Time Unsupervised Anomaly Detection


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This talk has been presented at RE-WORK Deep Learning Summit.

Abstract: Anomaly detection is a growing area of research in computer vision with many applications in industrial inspection, road traffic monitoring, medical diagnostics etc. However, the common supervised anomaly detection is not viable in practical systems. A more appealing approach is to collect only unlabeled anomaly-free images for train dataset i.e. to rely on an unsupervised anomaly detection. In this talk, I introduce our recent CFLOW-AD model that is based on a novel promising class of generative models called normalizing flows adopted for anomaly detection. Real-time CFLOW-AD is faster and smaller by a factor of 10x than prior models. Our experiments with the industrial MVTec dataset show that CFLOW-AD outperforms previous approaches both in detection and localization tasks.