Deep Learning for Anomaly Detection: A Review
It provides a comprehensive review for researchers and practitioners in anomaly detection, but it is incremental as it synthesizes existing work without introducing new methods.
This paper surveys deep learning methods for anomaly detection, reviewing advancements across three high-level and 11 fine-grained categories to address the complexities and challenges in the field.
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.