  Deep learning approaches to anomaly detection have recently improved the
state of the art in detection performance on complex datasets such as large
collections of images or text. These results have sparked a renewed interest in
the anomaly detection problem and led to the introduction of a great variety of
new methods. With the emergence of numerous such methods, including approaches
based on generative models, one-class classification, and reconstruction, there
is a growing need to bring methods of this field into a systematic and unified
perspective. In this review we aim to identify the common underlying principles
as well as the assumptions that are often made implicitly by various methods.
In particular, we draw connections between classic 'shallow' and novel deep
approaches and show how this relation might cross-fertilize or extend both
directions. We further provide an empirical assessment of major existing
methods that is enriched by the use of recent explainability techniques, and
present specific worked-through examples together with practical advice.
Finally, we outline critical open challenges and identify specific paths for
future research in anomaly detection.
