Papers › Deep Nearest Neighbor Anomaly Detection
Deep Nearest Neighbor Anomaly Detection
Liron Bergman, Niv Cohen, Yedid Hoshen
Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet). Self-supervised features however typically under-perform Imagenet pre-trained features. In this work, we investigate whether the recent progress can indeed outperform nearest-neighbor methods operating on an Imagenet pretrained feature space. The simple nearest-neighbor based-approach is experimentally shown to outperform self-supervised methods in: accuracy, few shot generalization, training time and noise robustness while making fewer assumptions on image distributions.
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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 | Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102 | DN2 CLIP ViT | Network | ViT | #3 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102 | DN2 CLIP ViT | ROC-AUC | 93.2 | #3 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200 | DN2 CLIP ViT | Network | ViT | #2 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200 | DN2 CLIP ViT | ROC-AUC | 93.8 | #2 of 5 | Archive leaderboard | report |
| Anomaly Detection | One-class CIFAR-10 | DN2 | AUROC | 92.5 | #14 of 36 | 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.
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