Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
This work addresses anomaly detection for industrial applications, offering an incremental improvement over existing methods.
The paper tackled the problem of anomaly detection in industrial images by proposing a two-stream decoder network and feature normality estimator to better discriminate anomalies, achieving state-of-the-art performance on a standard benchmark.
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to model normal features without seeing abnormal samples during training and then discriminating anomalies at test time based on the reconstructive errors. However, these models have limitations in reconstructing the abnormal samples due to their indiscriminate conveyance of features. Moreover, these approaches are not explicitly optimized for distinguishable anomalies. To address these problems, we propose a two-stream decoder network (TSDN), designed to learn both normal and abnormal features. Additionally, we propose a feature normality estimator (FNE) to eliminate abnormal features and prevent high-quality reconstruction of abnormal regions. Evaluation on a standard benchmark demonstrated performance better than state-of-the-art models.