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.
@article{arxiv.1909.02755,
title = {Image anomaly detection with capsule networks and imbalanced datasets},
author = {Claudio Piciarelli and Pankaj Mishra and Gian Luca Foresti},
journal= {arXiv preprint arXiv:1909.02755},
year = {2019}
}