Title: Are generative deep models for novelty detection truly better?,
Abstract: Many deep models have been recently proposed for anomaly detection. This
paper presents comparison of selected generative deep models and classical
anomaly detection methods on an extensive number of non--image benchmark
datasets. We provide statistical comparison of the selected models, in many
configurations, architectures and hyperparamaters. We arrive to conclusion that
performance of the generative models is determined by the process of selection
of their hyperparameters. Specifically, performance of the deep generative
models deteriorates with decreasing amount of anomalous samples used in
hyperparameter selection. In practical scenarios of anomaly detection, none of
the deep generative models systematically outperforms the kNN.