Explainable Anomaly Detection via Feature Based Localization
Deep learning has made remarkable progress in the field of image anomaly detection. An explanation of the validity of the detection is required for practical application. Conventional methods for localizing abnormal regions are mainly based on image reconstruction errors. However, they cannot directly extract specific features from abnormal regions, which limits localization performance. To address this issue, we developed a method for explainable anomaly detection in an unsupervised manner. We trained a feature extractor to extract features that had both the compactness of the normal state and the descriptiveness of the abnormal state for the input images and their reconstructed images. For explainability, our method localized and visualized abnormal regions by accumulating intermediate layers, which led to a significant difference in features extracted from the input image and the reconstructed image. The quantitative results of the defect segmentation and the qualitative results of anomaly localization from experiments on two datasets showed that our method outperformed conventional methods when it came to localizing abnormal regions.
ICANN