Deep Learning for Anomaly Detection: A Review. Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. ACM Computing Surveys, 54(2):38:1–38:38, March, 2021. ![Deep Learning for Anomaly Detection: A Review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{pang_deep_2021,
title = {Deep {Learning} for {Anomaly} {Detection}: {A} {Review}},
volume = {54},
issn = {0360-0300},
shorttitle = {Deep {Learning} for {Anomaly} {Detection}},
url = {https://doi.org/10.1145/3439950},
doi = {10.1145/3439950},
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.},
number = {2},
urldate = {2021-11-27},
journal = {ACM Computing Surveys},
author = {Pang, Guansong and Shen, Chunhua and Cao, Longbing and Hengel, Anton Van Den},
month = mar,
year = {2021},
keywords = {Anomaly detection, deep learning, novelty detection, one-class classification, outlier detection},
pages = {38:1--38:38},
}
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