  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.
