Normalizing Flows for Real-Time Unsupervised Anomaly Detection
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.
Denis Gudovskiy is a senior researcher at Panasonic AI lab in Mountain View. He specializes in deep learning-based algorithms for AI applications. His portfolio of research projects includes optimization of deep neural networks for edge AI devices, explainable AI tools, and automatic dataset management for computer vision applications. Denis received his M.Sc. in Computer Engineering from the University of Texas, Austin in 2008. Denis sees corporate research as an important layer between moonshot academia projects and clearly-defined product development roadmaps in business units. His goal is to find and promote viable academia-grade opportunities at Panasonic within the exponentially growing landscape of AI applications.