OOD Detection on CIFAR-10 (IND) vs SVHN (OOD)
0.9998AUROCMedix
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| MedixTraining Data=Pin and Pwild2025.10 | 0.9998 | — | — | 0.06 | |
| WOODSTraining Data=Pin and Pwild2025.10 | 0.9991 | — | — | 0.17 | |
| GradPCA-BatchBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9973 | — | — | 0.52 | |
| PALMMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.997 | — | — | 124 | |
| Energy + pNMLBackbone=WideResNet-402021.10 | 0.996 | 98.9 | 97.6 | — | |
| Hopfield BoostingMethod type=OE, Augmentations=Weak, Auxiliary outlier data=true, Backbone=ResNet-182024.05 | 0.9957 | — | — | 23 | |
| OETraining Data=Pin and Pwild2025.10 | 0.9953 | — | — | 1.13 | |
| KNNBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9942 | — | — | 1.99 | |
| SSD+Method type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.9941 | — | — | 305 | |
| KNN+Training Data=Pin only2025.10 | 0.9941 | — | — | 2.99 | |
| EnergyBackbone=WideResNet-402021.10 | 0.993 | 98.3 | 96.9 | — | |
| SPEM-noiseDensity Estimator=Glow2026.02 | 0.9916 | — | — | — | |
| GradPCA (block 4)Backbone=ResNetV2-50 (BiT-M)2025.05 | 0.9901 | — | — | 5.11 | |
| GAIA-ZBackbone=ResNet-34 (TIMM)2025.05 | 0.9879 | — | — | 4.39 | |
| GradPCA-VecBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9877 | — | — | 6.08 | |
| GradOrthBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9872 | — | — | 5.84 | |
| Energy (w/ OE)Training Data=Pin and Pwild2025.10 | 0.9872 | — | — | 5.24 | |
| Kernel PCA (CoRP)Backbone=ResNetV2-50 (BiT-M)2025.05 | 0.987 | — | — | 6.17 | |
| GradPCABackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9867 | — | — | 6.92 | |
| ASH-SBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9865 | — | — | 6.51 | |
| ASHTraining Data=Pin only2025.10 | 0.9865 | — | — | 6.51 | |
| NPOSMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.9837 | — | — | 904 | |
| MahalanobisBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9831 | — | — | 6.42 | |
| MahalanobisBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9819 | — | — | 9.07 | |
| GradPCA+DICEBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9796 | — | — | 10.98 | |
| Bootstrap DistillUncertainty Measure=Differential Entropy (Dent)2024.02 | 0.978 | — | — | — | |
| SPEMDensity Estimator=Glow2026.02 | 0.9768 | — | — | — | |
| MahalanobisTraining Data=Pin only2025.10 | 0.9762 | — | — | 12.89 | |
| BMUncertainty Measure=Mutual Information (MI)2024.02 | 0.975 | — | — | — | |
| CreDROEnsemble Number M=202026.02 | 0.974 | — | — | — | |
| CreDRO2026.02 | 0.974 | — | — | — | |
| CSITraining Data=Pin only2025.10 | 0.974 | — | — | 17.3 | |
| Bootstrap DistillUncertainty Measure=Mutual Information (MI)2024.02 | 0.972 | — | — | — | |
| CreDROEnsemble Number M=152026.02 | 0.971 | — | — | — | |
| GradPCA (block 3)Backbone=ResNet-34 (TIMM)2025.05 | 0.9701 | — | — | 15.07 | |
| BMUncertainty Measure=Differential Entropy (Dent)2024.02 | 0.97 | — | — | — | |
| CreDROEnsemble Number M=102026.02 | 0.97 | — | — | — | |
| END2Uncertainty Measure=Differential Entropy (Dent)2024.02 | 0.969 | — | — | — | |
| ASH-BBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9686 | — | — | 17.92 | |
| VRA-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9668 | — | — | 18.75 | |
| CreDROEnsemble Number M=52026.02 | 0.966 | — | — | — | |
| EDLUncertainty Measure=Differential Entropy (Dent)2024.02 | 0.965 | — | — | — | |
| EDLUncertainty Measure=Mutual Information (MI)2024.02 | 0.962 | — | — | — | |
| CreWraEnsemble Number M=152026.02 | 0.961 | — | — | — | |
| KNNTraining Data=Pin only2025.10 | 0.9596 | — | — | 24.53 | |
| GradPCA-BatchBackbone=ResNet-34 (TIMM)2025.05 | 0.9595 | — | — | 24.9 | |
| GradPCA-VecBackbone=ResNet-34 (TIMM)2025.05 | 0.9591 | — | — | 25.34 | |
| DICEBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.959 | — | — | 25.99 | |
| END2Uncertainty Measure=Mutual Information (MI)2024.02 | 0.958 | — | — | — | |
| EN-DROEnsemble Number M=202026.02 | 0.957 | — | — | — | |
| CreWraEnsemble Number M=202026.02 | 0.957 | — | — | — | |
| EN-DRO2026.02 | 0.957 | — | — | — | |
| CreWra2026.02 | 0.957 | — | — | — | |
| PostNetUncertainty Measure=Differential Entropy (Dent)2024.02 | 0.955 | — | — | — | |
| CreEns0.0Ensemble Number M=202026.02 | 0.955 | — | — | — | |
| CreEnsparameter=0.02026.02 | 0.955 | — | — | — | |
| CreEns0.0Ensemble Number M=152026.02 | 0.954 | — | — | — | |
| DICEBackbone=ResNet-34 (TIMM)2025.05 | 0.9538 | — | — | 16.01 | |
| EN-DROEnsemble Number M=52026.02 | 0.953 | — | — | — | |
| DEEnsemble Number M=152026.02 | 0.953 | — | — | — | |
| ASH-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9529 | — | — | 30.14 | |
| NCIBackbone=ResNet-34 (TIMM)2025.05 | 0.9522 | — | — | 23.65 | |
| EN-DROEnsemble Number M=152026.02 | 0.952 | — | — | — | |
| EnergyBackbone=ResNet-34 (TIMM)2025.05 | 0.9516 | — | — | 19.15 | |
| GAIA-ABackbone=ResNet-34 (TIMM)2025.05 | 0.9512 | — | — | 25.07 | |
| EN-DROEnsemble Number M=102026.02 | 0.951 | — | — | — | |
| Max logitsBackbone=ResNet-34 (TIMM)2025.05 | 0.9506 | — | — | 20.07 | |
| ODINBackbone=ResNet-34 (TIMM)2025.05 | 0.9506 | — | — | 20.07 | |
| CreEns0.0Ensemble Number M=102026.02 | 0.949 | — | — | — | |
| ASHMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.9486 | — | — | 2,517 | |
| Revisited PCABackbone=ResNet-34 (TIMM)2025.05 | 0.9485 | — | — | 24.08 | |
| DEEnsemble Number M=202026.02 | 0.948 | — | — | — | |
| CreRL1.0Ensemble Number M=202026.02 | 0.948 | — | — | — | |
| DE2026.02 | 0.948 | — | — | — | |
| CreRLparameter=1.02026.02 | 0.948 | — | — | — | |
| CreWraEnsemble Number M=102026.02 | 0.946 | — | — | — | |
| CreRL1.0Ensemble Number M=152026.02 | 0.946 | — | — | — | |
| ODINBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9457 | — | — | 25.29 | |
| PostNetUncertainty Measure=Mutual Information (MI)2024.02 | 0.944 | — | — | — | |
| CreRL1.0Ensemble Number M=52026.02 | 0.943 | — | — | — | |
| CreDEEnsemble Number M=202026.02 | 0.943 | — | — | — | |
| CreDE2026.02 | 0.943 | — | — | — | |
| KNNBackbone=ResNet-34 (TIMM)2025.05 | 0.943 | — | — | 37.25 | |
| NCIBackbone=ResNetV2-50 (BiT-M)2025.05 | 0.9425 | — | — | 23.15 | |
| GradNormBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9411 | — | — | 18.63 | |
| CreDEEnsemble Number M=152026.02 | 0.941 | — | — | — | |
| CreEns0.0Ensemble Number M=52026.02 | 0.94 | — | — | — | |
| Energy scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9399 | — | — | 40.61 | |
| MSPBackbone=ResNet-34 (TIMM)2025.05 | 0.9399 | — | — | 37.47 | |
| MoDEXsetting=standard, uncertainty_estimate=epistemic2026.05 | 0.9397 | — | — | — | |
| ReActBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9387 | — | — | 41.64 | |
| CreWraEnsemble Number M=52026.02 | 0.938 | — | — | — | |
| CreDEEnsemble Number M=102026.02 | 0.938 | — | — | — | |
| F-EDLArchitecture=VGG-16, Uncertainty Type=epistemic, Setting=classical2025.10 | 0.9374 | — | — | — | |
| F-EDLsetting=standard, uncertainty_estimate=epistemic2026.05 | 0.9374 | — | — | — | |
| Fisher-EDLUncertainty Measure=Differential Entropy (Dent)2024.02 | 0.937 | — | — | — | |
| CreRL1.0Ensemble Number M=102026.02 | 0.936 | — | — | — | |
| GENMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.9353 | — | — | 3,326 | |
| Softmax scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 0.9348 | — | — | 47.24 | |
| EBOMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 0.9343 | — | — | 3,210 |