Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Deep Learning
Anomaly Detection
outlier detection
novelty detection
one-class classification

Author(s) Name:  Guansong Pang , Chunhua Shen , Longbing Cao , Anton Van Den Hengel

Publisher name:  ACM

DOI:  10.1145/3439950

Volume Information:  Volume 54,Issue 2,March 2022,Article No.: 38,pp 1–38

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3439950