Deep learning for anomaly detection: A review

• Main Authors: PANG, Guansong, SHEN, Chunhua, CAO, Longbing, Van Den HENGEL, Anton
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Anomaly detection; deep learning; outlier detection; novelty detection; one-class classification; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7016; https://ink.library.smu.edu.sg/context/sis_research/article/8019/viewcontent/3439950.pdf
• Institution: Singapore Management University

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 article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 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.