OOD Detection on CIFAR-10 (IND) vs iSUN (OOD)
100AUROCDOE
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| DOEBackbone=ResNet-182024.05 | 100 | — | — | 0 | |
| DOSBackbone=ResNet-182024.05 | 99.99 | — | — | 0 | |
| EBO-OEBackbone=ResNet-182024.05 | 99.98 | — | — | 0 | |
| Hopfield BoostingBackbone=ResNet-182024.05 | 99.97 | — | — | 0 | |
| Hopfield BoostingMethod type=OE, Augmentations=Weak, Auxiliary outlier data=true, Backbone=ResNet-182024.05 | 99.97 | — | — | 0 | |
| MSP-OEBackbone=ResNet-182024.05 | 99.96 | — | — | 0 | |
| DALBackbone=ResNet-182024.05 | 99.93 | — | — | 0 | |
| DivOEBackbone=ResNet-182024.05 | 99.88 | — | — | 0 | |
| MixOEBackbone=ResNet-182024.05 | 99.87 | — | — | 0.17 | |
| POEMBackbone=ResNet-182024.05 | 99.87 | — | — | 0 | |
| Energy + pNMLBackbone=WideResNet-402021.10 | 99.4 | 98.7 | 97 | — | |
| EnergyBackbone=WideResNet-402021.10 | 99.3 | 98.3 | 96.7 | — | |
| ODINBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.9 | — | — | 3.98 | |
| ASH-SBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.9 | — | — | 5.17 | |
| GradPCA-BatchBackbone=ResNetV2-50 (BiT-M)2025.05 | 98.74 | — | — | 5.55 | |
| VRA-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.69 | — | — | 5.7 | |
| ASH-BBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.45 | — | — | 8.59 | |
| GradOrthBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.32 | — | — | 4.25 | |
| ASH-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.29 | — | — | 8.46 | |
| Energy scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 98.07 | — | — | 10.07 | |
| KNNBackbone=ResNetV2-50 (BiT-M)2025.05 | 98.03 | — | — | 9.49 | |
| ReActBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 97.72 | — | — | 12.72 | |
| DICEBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 97.55 | — | — | 4.36 | |
| GAIA-ZBackbone=ResNet-34 (TIMM)2025.05 | 97.47 | — | — | 12.56 | |
| MahalanobisBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 97.25 | — | — | 9.78 | |
| MahalanobisBackbone=ResNetV2-50 (BiT-M)2025.05 | 97.13 | — | — | 16.24 | |
| NCIBackbone=ResNetV2-50 (BiT-M)2025.05 | 96.95 | — | — | 15.94 | |
| GradPCA-VecBackbone=ResNetV2-50 (BiT-M)2025.05 | 96.84 | — | — | 17.05 | |
| GAIA-ABackbone=ResNet-34 (TIMM)2025.05 | 96.66 | — | — | 19.95 | |
| GradPCA (block 4)Backbone=ResNetV2-50 (BiT-M)2025.05 | 96.49 | — | — | 19.87 | |
| NCIBackbone=ResNet-34 (TIMM)2025.05 | 96.41 | — | — | 13.04 | |
| GradPCA (block 3)Backbone=ResNet-34 (TIMM)2025.05 | 96.32 | — | — | 20.92 | |
| NPOSMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 95.74 | — | — | 26.9 | |
| PALMMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 95.6 | — | — | 25.71 | |
| Revisited PCABackbone=ResNetV2-50 (BiT-M)2025.05 | 95.19 | — | — | 17.39 | |
| KNNBackbone=ResNet-34 (TIMM)2025.05 | 94.82 | — | — | 31.97 | |
| Proj. GradsBackbone=ResNetV2-50 (BiT-M)2025.05 | 94.78 | — | — | 24.13 | |
| Softmax scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 94.52 | — | — | 42.31 | |
| SSD+Method type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 94.49 | — | — | 35.71 | |
| Kernel PCA (CoRP)Backbone=ResNet-34 (TIMM)2025.05 | 94.19 | — | — | 36.9 | |
| GradPCABackbone=ResNet-34 (TIMM)2025.05 | 94.19 | — | — | 30.02 | |
| Revisited PCABackbone=ResNet-34 (TIMM)2025.05 | 93.29 | — | — | 30.22 | |
| GradOrthBackbone=ResNet-34 (TIMM)2025.05 | 93.1 | — | — | 32.6 | |
| GENMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 93.05 | — | — | 35.85 | |
| EBOMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 92.99 | — | — | 34.99 | |
| MaxLogitMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 92.88 | — | — | 36.02 | |
| GradPCABackbone=ResNetV2-50 (BiT-M)2025.05 | 92.74 | — | — | 37.65 | |
| GradPCA-VecBackbone=ResNet-34 (TIMM)2025.05 | 92.68 | — | — | 44.35 | |
| GradPCA-BatchBackbone=ResNet-34 (TIMM)2025.05 | 92.59 | — | — | 44.41 | |
| GAIA-ABackbone=ResNetV2-50 (BiT-M)2025.05 | 92.18 | — | — | 35.88 | |
| GradPCA+DICEBackbone=ResNetV2-50 (BiT-M)2025.05 | 92.12 | — | — | 41.67 | |
| EnergyBackbone=ResNet-34 (TIMM)2025.05 | 92.08 | — | — | 29.51 | |
| MSPMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 91.99 | — | — | 49.1 | |
| Max logitsBackbone=ResNet-34 (TIMM)2025.05 | 91.98 | — | — | 30.47 | |
| ODINBackbone=ResNet-34 (TIMM)2025.05 | 91.98 | — | — | 30.47 | |
| MahalanobisBackbone=ResNet-34 (TIMM)2025.05 | 91.83 | — | — | 47.78 | |
| GradPCA+DICEBackbone=ResNet-34 (TIMM)2025.05 | 91.7 | — | — | 36.85 | |
| GradNormBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 91.67 | — | — | 36.89 | |
| FedAvg + FOOGDalpha=0.12024.10 | 91.22 | — | — | 37.55 | |
| MSPBackbone=ResNet-34 (TIMM)2025.05 | 91.09 | — | — | 47.18 | |
| ReActBackbone=ResNet-34 (TIMM)2025.05 | 90.78 | — | — | 31.88 | |
| DICEBackbone=ResNet-34 (TIMM)2025.05 | 89.81 | — | — | 32.09 | |
| ASHMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 89.06 | — | — | 42.41 | |
| FedRoD + FOOGDalpha=0.12024.10 | 88.69 | — | — | 36.17 | |
| Kernel PCA (CoRP)Backbone=ResNetV2-50 (BiT-M)2025.05 | 87.93 | — | — | 58.07 | |
| MSPBackbone=ResNetV2-50 (BiT-M)2025.05 | 87.31 | — | — | 37.41 | |
| Proj. GradsBackbone=ResNet-34 (TIMM)2025.05 | 85.34 | — | — | 44.21 | |
| FedTHEalpha=0.12024.10 | 83.5 | — | — | 43.72 | |
| FedICONalpha=0.12024.10 | 82.95 | — | — | 49.98 | |
| FedRoDalpha=0.12024.10 | 82.83 | — | — | 43.4 | |
| ODINBackbone=ResNetV2-50 (BiT-M)2025.05 | 81.92 | — | — | 45.28 | |
| Max logitsBackbone=ResNetV2-50 (BiT-M)2025.05 | 81.91 | — | — | 45.28 | |
| GradOrthBackbone=ResNetV2-50 (BiT-M)2025.05 | 81.16 | — | — | 77.5 | |
| FedATOLalpha=0.12024.10 | 80.05 | — | — | 61.01 | |
| EnergyBackbone=ResNetV2-50 (BiT-M)2025.05 | 79.06 | — | — | 53.08 | |
| FedIIRalpha=0.12024.10 | 77.98 | — | — | 57.86 | |
| FedAvgalpha=0.12024.10 | 76.29 | — | — | 62.1 | |
| FOSTERalpha=0.12024.10 | 76.29 | — | — | 48.73 | |
| FedLNalpha=0.12024.10 | 76.03 | — | — | 66.41 | |
| GAIA-ZBackbone=ResNetV2-50 (BiT-M)2025.05 | 57.25 | — | — | 92.07 | |
| DICEBackbone=ResNetV2-50 (BiT-M)2025.05 | 44 | — | — | 99.88 | |
| ReActBackbone=ResNetV2-50 (BiT-M)2025.05 | 39.24 | — | — | 99.41 |