A Unifying Review of Deep and Shallow Anomaly Detection

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
that include 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 therefore 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 they exactly relate and moreover 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.

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