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Anomaly Detection On Unlabeled Cifar 10 Vs

Metrics

AUROC

Results

Performance results of various models on this benchmark

Model Name	AUROC	Paper Title	Repository
Input Complexity (PixelCNN++)	53.5	Input complexity and out-of-distribution detection with likelihood-based generative models	-
SSD	89.6	SSD: A Unified Framework for Self-Supervised Outlier Detection	-
MeanShifted	90.0	Mean-Shifted Contrastive Loss for Anomaly Detection	-
Likelihood (Glow)	58.2	Input complexity and out-of-distribution detection with likelihood-based generative models	-
PsudoLabels ResNet-18	90.8	Out-of-Distribution Detection Without Class Labels	-
CSI	89.3	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances	-
PsudoLabels ViT	96.7	Out-of-Distribution Detection Without Class Labels	-
PsudoLabels ResNet-152	93.3	Out-of-Distribution Detection Without Class Labels	-
Likelihood (PixelCNN++)	52.6	Input complexity and out-of-distribution detection with likelihood-based generative models	-
SCAN Features	90.2	Out-of-Distribution Detection Without Class Labels	-
Input Complexity (Glow)	73.6	Input complexity and out-of-distribution detection with likelihood-based generative models	-
GOAD	89.2	Classification-Based Anomaly Detection for General Data	-
MTL	82.92	Shifting Transformation Learning for Out-of-Distribution Detection	-

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Anomaly Detection On Unlabeled Cifar 10 Vs | SOTA | HyperAI