OOD Detection on LSUN-Resize (OOD) with CIFAR-10 (ID) (test)
0FPR@95Hopfield Boosting
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Hopfield BoostingBackbone=ResNet-182024.05 | 0 | 99.98 | |
| DOSBackbone=ResNet-182024.05 | 0 | 99.99 | |
| DOEBackbone=ResNet-182024.05 | 0 | 100 | |
| DivOEBackbone=ResNet-182024.05 | 0 | 99.89 | |
| DALBackbone=ResNet-182024.05 | 0 | 99.92 | |
| POEMBackbone=ResNet-182024.05 | 0 | 99.88 | |
| EBO-OEBackbone=ResNet-182024.05 | 0 | 99.98 | |
| MSP-OEBackbone=ResNet-182024.05 | 0 | 99.96 | |
| Hopfield BoostingMethod type=OE, Augmentations=Weak, Auxiliary outlier data=true, Backbone=ResNet-182024.05 | 0 | 99.98 | |
| MedixTraining Data=Pin and Pwild2025.10 | 0.01 | 99.98 | |
| WOODSTraining Data=Pin and Pwild2025.10 | 0.11 | 99.38 | |
| MixOEBackbone=ResNet-182024.05 | 0.16 | 99.89 | |
| OETraining Data=Pin and Pwild2025.10 | 0.54 | 98.84 | |
| GradOrthBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 2.33 | 98.71 | |
| ODINBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 3.09 | 99.02 | |
| GradNormBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 3.38 | 98.87 | |
| DICEBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 3.91 | 98.3 | |
| Energy (w/ OE)Training Data=Pin and Pwild2025.10 | 4.85 | 98.62 | |
| ASH-SBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 4.96 | 98.92 | |
| ASHTraining Data=Pin only2025.10 | 4.96 | 98.92 | |
| VRA-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 5.8 | 98.69 | |
| GradPCA-BatchBackbone=ResNetV2-50 (BiT-M)2025.05 | 6.48 | 98.67 | |
| ASH-PBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 7.97 | 98.33 | |
| ASH-BBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 8.13 | 98.54 | |
| MahalanobisBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 9.14 | 97.09 | |
| Energy scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 9.28 | 98.12 | |
| KNNBackbone=ResNetV2-50 (BiT-M)2025.05 | 9.77 | 98.11 | |
| KNN+Training Data=Pin only2025.10 | 11.22 | 97.98 | |
| ReActBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 11.46 | 97.87 | |
| CSITraining Data=Pin only2025.10 | 12.15 | 98.01 | |
| GAIA-ZBackbone=ResNet-34 (TIMM)2025.05 | 13.06 | 97.32 | |
| NCIBackbone=ResNetV2-50 (BiT-M)2025.05 | 14.1 | 96.71 | |
| MahalanobisBackbone=ResNetV2-50 (BiT-M)2025.05 | 17.36 | 97.04 | |
| GradPCA-VecBackbone=ResNetV2-50 (BiT-M)2025.05 | 17.94 | 96.82 | |
| GAIA-ABackbone=ResNet-34 (TIMM)2025.05 | 18.16 | 96.9 | |
| GradPCA (block 3)Backbone=ResNet-34 (TIMM)2025.05 | 19.63 | 96.54 | |
| GradPCA (block 4)Backbone=ResNetV2-50 (BiT-M)2025.05 | 20.02 | 96.49 | |
| Revisited PCABackbone=ResNetV2-50 (BiT-M)2025.05 | 23.08 | 94.16 | |
| Proj. GradsBackbone=ResNetV2-50 (BiT-M)2025.05 | 23.61 | 94.57 | |
| ODINTraining Data=Pin only2025.10 | 26.62 | 94.57 | |
| PALMMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 26.85 | 95.68 | |
| GradPCABackbone=ResNet-34 (TIMM)2025.05 | 27.09 | 95 | |
| Revisited PCABackbone=ResNet-34 (TIMM)2025.05 | 27.26 | 94.15 | |
| KNNTraining Data=Pin only2025.10 | 27.57 | 94.71 | |
| EnergyTraining Data=Pin only2025.10 | 27.58 | 94.24 | |
| EnergyBackbone=ResNet-34 (TIMM)2025.05 | 27.68 | 92.94 | |
| NCIBackbone=ResNet-34 (TIMM)2025.05 | 28.21 | 95.25 | |
| ODINBackbone=ResNet-34 (TIMM)2025.05 | 28.52 | 92.84 | |
| Max logitsBackbone=ResNet-34 (TIMM)2025.05 | 28.55 | 92.84 | |
| DICETraining Data=Pin only2025.10 | 28.93 | 93.56 | |
| ReActBackbone=ResNet-34 (TIMM)2025.05 | 29.37 | 91.89 | |
| GradOrthBackbone=ResNet-34 (TIMM)2025.05 | 29.71 | 94.05 | |
| KNNBackbone=ResNet-34 (TIMM)2025.05 | 29.99 | 95.33 | |
| GENMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 30.69 | 94.02 | |
| DICEBackbone=ResNet-34 (TIMM)2025.05 | 30.96 | 90.62 | |
| ASHMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 31.5 | 94.04 | |
| EBOMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 31.64 | 93.9 | |
| MaxLogitMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 32.62 | 92.84 | |
| GradPCA+DICEBackbone=ResNet-34 (TIMM)2025.05 | 33.33 | 92.87 | |
| Kernel PCA (CoRP)Backbone=ResNet-34 (TIMM)2025.05 | 33.54 | 94.81 | |
| ReActTraining Data=Pin only2025.10 | 33.63 | 93.58 | |
| NPOSMethod type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 34.3 | 94.78 | |
| MSPBackbone=ResNetV2-50 (BiT-M)2025.05 | 35.85 | 87.58 | |
| GAIA-ABackbone=ResNetV2-50 (BiT-M)2025.05 | 35.86 | 92.51 | |
| GradPCABackbone=ResNetV2-50 (BiT-M)2025.05 | 36.76 | 92.97 | |
| SSD+Method type=Training, Augmentations=Strong, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 38.18 | 90.39 | |
| GradPCA+DICEBackbone=ResNetV2-50 (BiT-M)2025.05 | 41.07 | 92.2 | |
| Proj. GradsBackbone=ResNet-34 (TIMM)2025.05 | 41.66 | 86.85 | |
| Softmax scoreBackbone=DenseNet, In-Distribution Dataset=CIFAR-102023.08 | 42.1 | 94.51 | |
| ODINBackbone=ResNetV2-50 (BiT-M)2025.05 | 42.29 | 83.16 | |
| Max logitsBackbone=ResNetV2-50 (BiT-M)2025.05 | 42.32 | 83.15 | |
| GradPCA-BatchBackbone=ResNet-34 (TIMM)2025.05 | 42.32 | 93.29 | |
| GradPCA-VecBackbone=ResNet-34 (TIMM)2025.05 | 42.47 | 93.32 | |
| MahalanobisTraining Data=Pin only2025.10 | 42.62 | 93.23 | |
| MahalanobisBackbone=ResNet-34 (TIMM)2025.05 | 45.49 | 92.2 | |
| MSPBackbone=ResNet-34 (TIMM)2025.05 | 45.9 | 91.82 | |
| EnergyBackbone=ResNetV2-50 (BiT-M)2025.05 | 49.87 | 80.69 | |
| MSPTraining Data=Pin only2025.10 | 52.15 | 91.37 | |
| Kernel PCA (CoRP)Backbone=ResNetV2-50 (BiT-M)2025.05 | 55.99 | 89.45 | |
| MSPMethod type=Post-hoc, Augmentations=Weak, Auxiliary outlier data=false, Backbone=ResNet-182024.05 | 57.58 | 87.27 | |
| GradOrthBackbone=ResNetV2-50 (BiT-M)2025.05 | 78.2 | 80.29 | |
| GAIA-ZBackbone=ResNetV2-50 (BiT-M)2025.05 | 97.72 | 48.22 | |
| ReActBackbone=ResNetV2-50 (BiT-M)2025.05 | 99.59 | 38.91 | |
| DICEBackbone=ResNetV2-50 (BiT-M)2025.05 | 99.88 | 44.5 |