Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
CFLOW-AD CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsWACV 2022 preprint:https://arxiv.org/abs/2107.12571 Abstract Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model […]
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