Reconstruction Error-based Anomaly Detection with Few Outlying Examples
This work addresses a specific issue in semi-supervised anomaly detection for domains where labeled anomalies are scarce, offering an incremental improvement over existing methods.
The paper tackles the problem of reconstruction error-based anomaly detection methods often reconstructing anomalies well, especially when anomalies are present in the training set, by proposing a strategy that uses a few labeled anomalous examples to increase the contrast in reconstruction errors between normal and anomalous data, resulting in better performance than standard autoencoders and other deep learning techniques for semi-supervised anomaly detection.
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.