Enforced Isolation Deep Network For Anomaly Detection In Images

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.