Papers › Zero-Shot Anomaly Detection via Batch Normalization
Zero-Shot Anomaly Detection via Batch Normalization
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
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 theoretical results guarantee the zero-shot generalization for unseen AD tasks; our empirical 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. Code is at https://github.com/aodongli/zero-shot-ad-via-batch-norm
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | ACR (zero-shot) | Detection AUROC | 85.8 | #121 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | ACR (zero-shot) | Segmentation AP | 38.9 | #121 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | ACR (zero-shot) | Segmentation AUPRO | 72.7 | #121 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | ACR (zero-shot) | Segmentation AUROC | 92.5 | #121 of 148 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | AnoShift | ACR-NTL (zero-shot, test anomaly ratio=1%) | ROC-AUC FAR | 62.5 | #1 of 15 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | AnoShift | ACR-DSVDD (zero-shot, anomaly ratio=1%) | ROC-AUC FAR | 62 | #2 of 15 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | AnoShift | ACR-NTL (zero-shot, test anomaly ratio=20%) | ROC-AUC FAR | 62 | #3 of 15 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | AnoShift | ACR-DSVDD (zero-shot, anomaly ratio=20%) | ROC-AUC FAR | 59.1 | #4 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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