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SotA/Methodology/Anomaly Detection/Leave-One-Class-Out ImageNet-30

Anomaly Detection on Leave-One-Class-Out ImageNet-30

Metric: AUROC (higher is better)

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#Model↕AUROC▼AugmentationsPaperDate↕Code
1BCE-CLIP (OE)99.3YesExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code
2CLIP (zero shot)97.8NoExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code
3DSAD88.8YesExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code
4HSC (OE)88.3YesExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code
5Binary Cross Entropy (OE)88.2YesExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code
6DSVDD49.7NoExposing Outlier Exposure: What Can Be Learned F...2022-05-23Code