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Anomaly Detection On One Class Imagenet 30

Metrics

AUROC

Results

Performance results of various models on this benchmark

		Paper Title	Repository
BCE-Clip (OE)	99.90	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CLIP (Zero Shot)	99.88	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Binary Cross Entropy (OE)	97.7	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CSI	91.6	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
FCDD	91	Explainable Deep One-Class Classification
RotNet + Translation + Self-Attention + Resize	85.7	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation + Self-Attention	84.8	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Self-Attention	81.6	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation	77.9	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet	65.3	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Supervised (OE)	56.1	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

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Anomaly Detection On One Class Imagenet 30 | SOTA | HyperAI