Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification
Seungmi Oh,
Jeongtae Kim
Abstract:We propose a novel deep learning-based anomaly detection (AD) system that combines a pixelwise classification network with conditional normalizing flow (CNF) networks by sharing feature extractors. We trained the pixelwise classification network using synthetic abnormal data to fine-tune a pretrained feature extractor of the CNF networks, thereby learning the discriminative features of the indomain data. After that, we trained the CNF networks using normal data with the fine-tuned feature extractor to estimate… Show more
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