Papers › Anomaly Detection with Conditioned Denoising Diffusion Models
Anomaly Detection with Conditioned Denoising Diffusion Models
Arian Mousakhan, Thomas Brox, Jawad Tayyub
Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image. This ensures a coherent restoration that closely resembles the target image. Our anomaly detection framework employs the conditioning mechanism, where the target image is set as the input image to guide the denoising process, leading to a defectless reconstruction while maintaining nominal patterns. Anomalies are then localised via a pixel-wise and feature-wise comparison of the input and reconstructed image. Finally, to enhance the effectiveness of the feature-wise comparison, we introduce a domain adaptation method that utilises nearly identical generated examples from our conditioned denoising process to fine-tune the pretrained feature extractor. The veracity of DDAD is demonstrated on various datasets including MVTec and VisA benchmarks, achieving state-of-the-art results of 99.8 % and 98.9 % image-level AUROC respectively.
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Code
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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 | DDAD | Detection AUROC | 99.8 | #6 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | DDAD | Segmentation AUROC | 98.1 | #6 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | DDAD | Detection AUROC | 98.9 | #6 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | DDAD | Segmentation AUPRO (until 30% FPR) | 92.7 | #6 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | DDAD | Segmentation AUROC | 97.6 | #6 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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