Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text. These results have sparked a renewed interest in the AD problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review, we aim to identify the common underlying principles and the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic “shallow” and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that are enriched by the use of recent explainability techniques and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in AD.

中文翻译：

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深度和浅层异常检测的统一回顾

深度学习的异常检测方法（AD）最近改进了对复杂数据集（例如大量图像或文本）的检测性能的最新技术水平。这些结果激发了人们对广告问题的新兴趣，并导致了许​​多新方法的引入。随着大量此类方法的出现，包括基于生成模型的方法，一类分类和重构，越来越需要将该领域的方法带入系统且统一的视角。在这篇综述中，我们旨在确定常见的基本原理和假设，这些原则和假设通常是通过各种方法隐含地做出的。特别是，我们在经典的“浅层”方法和新颖的深层方法之间建立了联系，并说明了这种关系如何交叉施肥或扩展两个方向。我们还提供了对主要现有方法的实证评估，这些方法通过使用最新的可解释性技术得到了充实，并提供了具体的实例化示例以及实用建议。最后，我们概述了关键的开放挑战，并确定了AD未来研究的特定路径。