  Anomaly detection, a.k.a. outlier detection or novelty detection, has been a
lasting yet active research area in various research communities for several
decades. There are still some unique problem complexities and challenges that
require advanced approaches. In recent years, deep learning enabled anomaly
detection, i.e., deep anomaly detection, has emerged as a critical direction.
This paper surveys the research of deep anomaly detection with a comprehensive
taxonomy, covering advancements in three high-level categories and 11
fine-grained categories of the methods. We review their key intuitions,
objective functions, underlying assumptions, advantages and disadvantages, and
discuss how they address the aforementioned challenges. We further discuss a
set of possible future opportunities and new perspectives on addressing the
challenges.
