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.

中文翻译：

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用于异常检测的深度学习

几十年来，异常检测，又称异常值检测或新奇检测，一直是各个研究社区中一个持久而活跃的研究领域。仍然有一些独特的问题复杂性和挑战需要先进的方法。近年来，深度学习使异常检测成为可能，即深度异常检测, 已成为一个关键方向。本文以全面的分类法综述了深度异常检测的研究，涵盖了方法的 3 个高级类别和 11 个细粒度类别的进展。我们回顾了它们的关键直觉、目标函数、基本假设、优点和缺点，并讨论了它们如何应对上述挑战。我们进一步讨论了一系列可能的未来机遇和应对挑战的新观点。