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SOTA
异常检测
Anomaly Detection On Unlabeled Cifar 10 Vs
Anomaly Detection On Unlabeled Cifar 10 Vs
评估指标
AUROC
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AUROC
Paper Title
Repository
PsudoLabels ViT
96.7
Out-of-Distribution Detection Without Class Labels
-
PsudoLabels ResNet-152
93.3
Out-of-Distribution Detection Without Class Labels
-
PsudoLabels ResNet-18
90.8
Out-of-Distribution Detection Without Class Labels
-
SCAN Features
90.2
Out-of-Distribution Detection Without Class Labels
-
MeanShifted
90.0
Mean-Shifted Contrastive Loss for Anomaly Detection
SSD
89.6
SSD: A Unified Framework for Self-Supervised Outlier Detection
CSI
89.3
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
GOAD
89.2
Classification-Based Anomaly Detection for General Data
MTL
82.92
Shifting Transformation Learning for Out-of-Distribution Detection
-
Input Complexity (Glow)
73.6
Input complexity and out-of-distribution detection with likelihood-based generative models
Likelihood (Glow)
58.2
Input complexity and out-of-distribution detection with likelihood-based generative models
Input Complexity (PixelCNN++)
53.5
Input complexity and out-of-distribution detection with likelihood-based generative models
Likelihood (PixelCNN++)
52.6
Input complexity and out-of-distribution detection with likelihood-based generative models
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