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.

Image Anomaly Detection with Capsule Networks and Imbalanced Datasets

Piciarelli, Claudio;Mishra, Pankaj;Foresti, Gian Luca
2019

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1177500
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