Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification

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 in-domain data. After that, we trained the CNF networks using normal data with the fine-tuned feature extractor to estimate the density of normal data. During inference, we detected anomalies by calculating the weighted average of the anomaly scores from the pixelwise classification and CNF networks. Because the proposed system not only has learned the properties of in-domain data but also aggregated the anomaly scores of the classification and CNF networks, it showed significantly improved performance compared to existing methods in experiments using the MvTecAD and BTAD datasets. Moreover, the proposed system does not increase computations intensively since the classification and the density estimation systems share feature extractors.

Keywords

• Industrial inspection
• machine vision
• deep learning
• anomaly detection
• synthetic defect generation
• density estimation