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Reconstruction error-based neural architectures constitute a classical deeplearning approach to anomaly detection which has shown great performances. Itconsists in training an Autoencoder to reconstruct a set of examples deemed torepresent the normality and then to point out as anomalies those data that showa sufficiently large reconstruction error. Unfortunately, these architecturesoften become able to well reconstruct also the anomalies in the data. Thisphenomenon is more evident when there are anomalies in the training set. Inparticular when these anomalies are labeled, a setting called semi-supervised,the best way to train Autoencoders is to ignore anomalies and minimize thereconstruction error on normal data. The goal of this work is to investigateapproaches to allow reconstruction error-based architectures to instruct themodel to put known anomalies outside of the domain description of the normaldata. Specifically, our strategy exploits a limited number of anomalousexamples to increase the contrast between the reconstruction error associatedwith normal examples and those associated with both known and unknownanomalies, thus enhancing anomaly detection performances. The experiments showthat this new procedure achieves better performances than the standardAutoencoder approach and the main deep learning techniques for semi-supervisedanomaly detection.

Reconstruction Error-based Anomaly Detection with Few Outlying Examples