Factorized Industrial Anomaly Detection and Localization

Abstract

Convolutional neural networks trained on large datasets can generalize various down-streaming tasks, including industrial anomaly detection and localization, which is critical in modern large-scale industrial manufacturing. Whereas previous methods have demonstrated that the feature fusion strategy across multiple layers is effective for better performance on industrial anomaly detection and localization, they lack flexibility in intervening and manipulating the local and global information composition process. Through experiments, we demonstrate that the brute-force feature fusion strategy used in previous methods leads to sub-optimal performance in most industrial anomaly detection scenarios. To this end, we propose a novel feature factorization and reversion framework based on invertible neural networks, enabling the selective emphasis or suppression of distinct information in a continuous space by request to fit various preferences for detecting different abnormalities. The preferred local and global info-combination for detecting different defects on 15 objects is studied by experiments exhaustively on the popular benchmark MVTec-AD. Based on feature factorization and reversion, our method is able to outperform previous state-of-the-art methods by a noticeable margin, achieving an image-level anomaly detection AUROC score of up to 99.67% (previously 99.4%), pixel-level anomaly localization AUROC score of 98.61% (previously 98.5%), and AUPRO of 95.15% (previously 94.6%), which validates the effectiveness of the proposed method for industrial anomaly detection and localization.