  Deep generative models are challenging the classical methods in the field of
anomaly detection nowadays. Every new method provides evidence of outperforming
its predecessors, often with contradictory results. The objective of this
comparison is twofold: to compare anomaly detection methods of various
paradigms with focus on deep generative models, and identification of sources
of variability that can yield different results. The methods were compared on
popular tabular and image datasets. We identified the main sources of
variability to be experimental conditions: i) the type data set (tabular or
image) and the nature of anomalies (statistical or semantic), and ii) strategy
of selection of hyperparameters, especially the number of available anomalies
in the validation set. Different methods perform the best in different
contexts, i.e. combination of experimental conditions together with
computational time. This explains the variability of the previous results and
highlights the importance of careful specification of the context in the
publication of a new method. All our code and results are available for
download.
