Zero-Shot Anomaly Detection via Batch Normalization

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.

Authors

• Aodong Li Amazon Web Services
• Chen Qiu
• Marius Kloft Technische Universität Kaiserslautern
• Padhraic Smyth University of California, Irvine
• Maja Rudolph
• Stephan Mandt

Keywords

No keywords are indexed for this paper.