Enforced Isolation Deep Network For Anomaly Detection In Images

Abstract

Challenges in anomaly detection include the implicit definition of anomaly, benchmarking against human intuition and scarcity of anomalous examples. We introduce a novel approach designed to enforce separation of normal and abnormal samples in an embedded space using a refined Triple Loss Function, within the paradigm of Deep Networks. Training is based on randomly sampled triplets to manage datasets with small proportion of anomalous data. Results for a range of proportions between normal and anomalous data are presented on the MNIST, CIFAR10 and Concrete Cracks datasets and compared against the current state of the art.