Papers › Mean-Shifted Contrastive Loss for Anomaly Detection
Mean-Shifted Contrastive Loss for Anomaly Detection
Tal Reiss, Yedid Hoshen
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection performance can be significantly improved by fine-tuning the pre-trained representations on the normal training images. In this paper, we first demonstrate and analyze that contrastive learning, the most popular self-supervised learning paradigm cannot be naively applied to pre-trained features. The reason is that pre-trained feature initialization causes poor conditioning for standard contrastive objectives, resulting in bad optimization dynamics. Based on our analysis, we provide a modified contrastive objective, the Mean-Shifted Contrastive Loss. Our method is highly effective and achieves a new state-of-the-art anomaly detection performance including 98.6% ROC-AUC on the CIFAR-10 dataset.
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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 | Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) | MeanShifted | Network | ResNet-152 | #5 of 8 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) | MeanShifted | ROC-AUC | 92.6 | #5 of 8 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Mean-Shifted Contrastive Loss | Detection AUROC | 87.2 | #117 of 148 | Archive leaderboard | report |
| Anomaly Detection | One-class CIFAR-10 | Mean-Shifted Contrastive Loss | AUROC | 98.6 | #6 of 36 | Archive leaderboard | report |
| Anomaly Detection | One-class CIFAR-100 | Mean-Shifted Contrastive Loss | AUROC | 96.5 | #4 of 15 | Archive leaderboard | report |
| Anomaly Detection | Unlabeled CIFAR-10 vs CIFAR-100 | MeanShifted | AUROC | 90.0 | #5 of 13 | Archive leaderboard | report |
| Anomaly Detection | Unlabeled CIFAR-10 vs CIFAR-100 | MeanShifted | Network | ResNet-152 | #5 of 13 | 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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