  Image anomaly detection consists in finding images with anomalous, unusual
patterns with respect to a set of normal data. Anomaly detection can be applied
to several fields and has numerous practical applications, e.g. in industrial
inspection, medical imaging, security enforcement, etc.. However, anomaly
detection techniques often still rely on traditional approaches such as
one-class Support Vector Machines, while the topic has not been fully developed
yet in the context of modern deep learning approaches. In this paper, we
propose an image anomaly detection system based on capsule networks under the
assumption that anomalous data are available for training but their amount is
scarce.
