Deep Learning for Anomaly Detection

By Guansong Pang, Chunhua Shen, Longbing Cao, Anton van den Hengel. ACM Computing Surveys, 2020

1,385 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/775247047d0b56950ba5ea77d4a29772eca95c1b

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In Computer Networks and Communications

Citations in the corpus

Machine Learning in Aerodynamic Shape Optimization (2022, background): “Unsupervised learning has prevailed in various important problems, such as anomaly detection [173, 174] and novelty detection [175, 176], clustering [177, 178], and dimensionality reduction[179, 180].”
AI Autonomy: Self-initiated Open-world Continual Learning and Adaptation (2022, background): “A fair amount of research has been done on this (see the surveys (Pang et al. 2021; Parmar et al. 2021; Yang et al. 2021)).”