Papers › CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional...
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Denis Gudovskiy, Shun Ishizaka, Kazuki Kozuka
Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments.
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Code
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Code Syntology ran Syntology
12 samples harvested; 3 ran; 1 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | GoodsAD | CFLOW-AD | AUPR | 75.3 | #4 of 11 | Archive leaderboard | report |
| Anomaly Classification | GoodsAD | CFLOW-AD | AUROC | 71.2 | #4 of 11 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CFLOW-AD | Detection AUROC | 98.26 | #63 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CFLOW-AD | FPS | 27 | #63 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CFLOW-AD | Segmentation AUPRO | 94.6 | #63 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CFLOW-AD | Segmentation AUROC | 98.62 | #63 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | CFLOW | Detection AUROC | 91.5 | #28 of 50 | 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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