Home News Latest Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

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	-

0 of 13 row(s) selected.