Reconstruction Error-based Anomaly Detection with Few Outlying Examples

TL;DR

This paper proposes a method to improve anomaly detection by using limited known anomalies to better distinguish normal data from anomalies through reconstruction error, outperforming standard autoencoder approaches.

Contribution

It introduces a novel approach that leverages few known anomalies to enhance the contrast in reconstruction errors, improving semi-supervised anomaly detection performance.

Findings

Outperforms standard autoencoder methods in anomaly detection.

Utilizes limited anomalous examples to improve model discrimination.

Achieves better results than existing deep learning semi-supervised techniques.

Abstract

Reconstruction error-based neural architectures constitute a classical deep learning approach to anomaly detection which has shown great performances. It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a sufficiently large reconstruction error. Unfortunately, these architectures often become able to well reconstruct also the anomalies in the data. This phenomenon is more evident when there are anomalies in the training set. In particular when these anomalies are labeled, a setting called semi-supervised, the best way to train Autoencoders is to ignore anomalies and minimize the reconstruction error on normal data. The goal of this work is to investigate approaches to allow reconstruction error-based architectures to instruct the model to put known anomalies outside of the domain description of the normal data. Specifically, our strategy exploits a limited number of anomalous examples to increase the contrast between the reconstruction error associated with normal examples and those associated with both known and unknown anomalies, thus enhancing anomaly detection performances. The experiments show that this new procedure achieves better performances than the standard Autoencoder approach and the main deep learning techniques for semi-supervised anomaly detection.