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      Deep Learning for Anomaly Detection: A Review

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          Abstract

          Anomaly detection, a.k.a. outlier 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 reviews the research of deep anomaly detection with a comprehensive taxonomy of detection methods, 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.

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          Author and article information

          Journal
          05 July 2020
          Article
          2007.02500
          87ee1189-8878-4583-bf1f-fe061ce188cd

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          Survey paper, 36 pages, 180 references, 2 figures, 3 tables
          cs.LG cs.CV stat.ML

          Computer vision & Pattern recognition,Machine learning,Artificial intelligence

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