Reconstruction Error-based Anomaly Detection with Few Outlying Examples
Fabrizio Angiulli Fabio Fassetti Luca Ferragina
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.