Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The challenge of adapting an anomaly detector to drift in the normal data distribution, especially when no training data is available for the "new normal," has led to the development of zero-shot AD techniques. In this paper, we propose a simple yet effective method called Adaptive Centered Representations (ACR) for zero-shot batch-level AD. Our approach trains off-the-shelf deep anomaly detectors (such as deep SVDD) to adapt to a set of inter-related training data distributions in combination with batch normalization, enabling automatic zero-shot generalization for unseen AD tasks. This simple recipe, batch normalization plus meta-training, is a highly effective and versatile tool. Our results demonstrate the first zero-shot AD results for tabular data and outperform existing methods in zero-shot anomaly detection and segmentation on image data from specialized domains.

翻译：异常检测（AD）在许多安全关键应用领域中起着至关重要的作用。由于异常检测器需要适应正常数据分布的变化（尤其是在无法获取“新常态”训练数据的情况下），这推动了零样本异常检测技术的发展。本文提出了一种简单而有效的方法——自适应中心表示（ACR），用于零样本批量级异常检测。我们的方法将现成的深度异常检测器（如深度SVDD）与批归一化相结合，使其适应一组相互关联的训练数据分布，从而实现对未见异常检测任务的自动零样本泛化。这种简单的策略——批归一化加元训练——是一种高效且通用的工具。我们的结果首次在表格数据上实现了零样本异常检测，并在专业领域的图像数据上，在零样本异常检测与分割任务中优于现有方法。