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Anomaly Detection On One Class Imagenet 30

Metriken

AUROC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
AUROC
Paper TitleRepository
RotNet + Translation77.9Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation + Self-Attention84.8Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet65.3Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
CSI91.6CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
RotNet + Translation + Self-Attention + Resize85.7Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
FCDD91Explainable Deep One-Class Classification
CLIP (Zero Shot)99.88Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
BCE-Clip (OE)99.90Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
RotNet + Self-Attention81.6Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Supervised (OE)56.1Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Binary Cross Entropy (OE)97.7Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
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Anomaly Detection On One Class Imagenet 30 | SOTA | HyperAI