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@article{pangDeepLearningAnomaly2022,
  title = {Deep {{Learning}} for {{Anomaly Detection}}: {{A Review}}},
  shorttitle = {Deep {{Learning}} for {{Anomaly Detection}}},
  author = {Pang, Guansong and Shen, Chunhua and Cao, Longbing and family=Hengel, given=Anton, prefix=van den, useprefix=false},
  date = {2022-03-31},
  journaltitle = {ACM Computing Surveys},
  shortjournal = {ACM Comput. Surv.},
  volume = {54},
  number = {2},
  eprint = {2007.02500},
  eprinttype = {arXiv},
  eprintclass = {cs},
  pages = {1--38},
  issn = {0360-0300, 1557-7341},
  doi = {10.1145/3439950},
  url = {http://arxiv.org/abs/2007.02500},
  urldate = {2025-07-01},
  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 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.},
  langid = {english},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/leo/Zotero/storage/ZE9DZELX/Pang et al. - 2022 - Deep Learning for Anomaly Detection A Review.pdf}
}
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This is an article in Russian \parencite{pangDeepLearningAnomaly2022}.

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