Measuring internal inequality in capsule networks for supervised anomaly detection

In this paper we explore the use of income inequality metrics such as Gini or Palma coefficients as a tool to identify anomalies via capsule networks. We demonstrate how the interplay between primary and class capsules gives rise to differences in behavior regarding anomalous and normal input which can be exploited to detect anomalies. Our setup for anomaly detection requires supervision in a form of known outliers. We derive several criteria for capsule networks and apply them to a number of Computer Vision benchmark datasets (MNIST, Fashion-MNIST, Kuzushiji-MNIST and CIFAR10), as well as to the dataset of skin lesion images (HAM10000) and the dataset of CRISPR-Cas9 off-target pairs. The proposed methods outperform the competitors in the majority of considered cases.