Enhancing anomaly detectors with LatentOut

\({{\textbf{Latent}}\varvec{Out}}\) is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of \({{\textbf{Latent}}\varvec{Out}}\) acting as a one-class classifier and we experiment the combination of \({{\textbf{Latent}}\varvec{Out}}\) with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by \({{\textbf{Latent}}\varvec{Out}}\) has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.