Unsupervised Two-Stage Anomaly Detection
This addresses the challenge of detecting unpredictable anomalies in images using only anomaly-free data, which is incremental as it builds on existing AutoEncoder-based methods.
The paper tackles the problem of unsupervised anomaly detection in images by proposing a two-stage approach that generates high-fidelity yet anomaly-free reconstructions, outperforming state-of-the-art methods on four datasets with real-world objects and textures.
Anomaly detection from a single image is challenging since anomaly data is always rare and can be with highly unpredictable types. With only anomaly-free data available, most existing methods train an AutoEncoder to reconstruct the input image and find the difference between the input and output to identify the anomalous region. However, such methods face a potential problem - a coarse reconstruction generates extra image differences while a high-fidelity one may draw in the anomaly. In this paper, we solve this contradiction by proposing a two-stage approach, which generates high-fidelity yet anomaly-free reconstructions. Our Unsupervised Two-stage Anomaly Detection (UTAD) relies on two technical components, namely the Impression Extractor (IE-Net) and the Expert-Net. The IE-Net and Expert-Net accomplish the two-stage anomaly-free image reconstruction task while they also generate intuitive intermediate results, making the whole UTAD interpretable. Extensive experiments show that our method outperforms state-of-the-arts on four anomaly detection datasets with different types of real-world objects and textures.