Anomaly detection (AD) tries to identify data instances that deviate from the norm in a given data set. Since data distributions are subject to distribution shifts, our concept of ``normality" may also drift, raising the need for zero-shot adaptation approaches for anomaly detection. However, the fact that current zero-shot AD methods rely on foundation models that are restricted in their domain (natural language and natural images), are costly, and oftentimes proprietary, asks for alternative approaches. In this paper, we propose a simple and highly effective zero-shot AD approach compatible with a variety of established AD methods. Our solution relies on training an off-the-shelf anomaly detector (such as a deep SVDD) on a set of inter-related data distributions in combination with batch normalization. This simple recipe--batch normalization plus meta-training--is a highly effective and versatile tool. Our results demonstrate the first zero-shot anomaly detection results for tabular data and SOTA zero-shot AD results for image data from specialized domains.

翻译：异常检测（AD）旨在识别数据集中偏离正常模式的数据实例。由于数据分布会经历分布漂移，我们对“正常性”的定义也可能发生漂移，因此需要开发面向异常检测的零样本自适应方法。然而，当前零样本异常检测方法依赖于基础模型，这些模型受限于其领域（自然语言和自然图像）、成本高昂、且通常为专有技术，这促使我们探索替代方案。本文提出一种简单且高效的零样本异常检测方法，可兼容多种现有异常检测技术。我们的解决方案基于将批量归一化与基于一组相互关联数据分布的离线异常检测器（如深度SVDD）相结合进行训练。这种简单的策略——批量归一化加元训练——是一种高效且通用的工具。我们的实验结果表明，这是首次在表格数据上实现零样本异常检测，并在专业领域图像数据上取得了最优的零样本异常检测结果。