Out-of-Distribution Detection on CIFAR10 (ID) vs SVHN (OOD)
100AUROCERD
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| ERDEnsemble size=52020.12 | 100 | 100 | — | |
| ERD++Ensemble size=52020.12 | 100 | 100 | — | |
| DynProtoBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 100 | — | 0 | |
| AdaNDBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 99.97 | — | 0.15 | |
| DynProtoBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 99.74 | — | 1.49 | |
| CIDERContrastive Learning=Yes2026.01 | 99.72 | — | 2.89 | |
| DALBackbone=ResNet-182024.05 | 99.61 | — | 125 | |
| KNN+Contrastive Learning=Yes2026.01 | 99.61 | — | 2.7 | |
| DOEBackbone=ResNet-182024.05 | 99.6 | — | 197 | |
| Hopfield BoostingBackbone=ResNet-182024.05 | 99.57 | — | 23 | |
| SSD+Contrastive Learning=Yes2026.01 | 99.51 | — | 2.47 | |
| POEMBackbone=ResNet-182024.05 | 99.33 | — | 148 | |
| ViMBackbone=DenseNet-1002026.03 | 99.29 | — | 3.39 | |
| CE + SimCLRContrastive Learning=Yes2026.01 | 99.22 | — | 6.98 | |
| MSP-OEBackbone=ResNet-182024.05 | 99.2 | — | 431 | |
| CADRefBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 99.17 | — | 4.17 | |
| DOSBackbone=ResNet-182024.05 | 99.15 | — | 309 | |
| EBO-OEBackbone=ResNet-182024.05 | 99.15 | — | 266 | |
| ViTBackbone=ViT-S-16, Pre-trained=Imagenet21k2020.12 | 99 | 98 | — | |
| Prototype FusionBackbone=ResNet-182026.03 | 98.94 | — | 5.92 | |
| MCMBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 98.84 | — | 3.6 | |
| Prototype FusionBackbone=DenseNet-1002026.03 | 98.74 | — | 6.53 | |
| ASH-SBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 98.74 | — | 6.16 | |
| DivOEBackbone=ResNet-182024.05 | 98.53 | — | 621 | |
| VIMBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 98.46 | — | 8.65 | |
| ESOODBackbone=ResNet-182026.03 | 98.19 | — | 8.67 | |
| LaRExBackbone=DenseNet-1002026.03 | 97.99 | — | 10.15 | |
| GLMCMBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 97.94 | — | 9.5 | |
| ESOODBackbone=DenseNet-1002026.03 | 97.89 | — | 11.32 | |
| MahalanobisContrastive Learning=No2026.01 | 97.8 | — | 9.24 | |
| NegRefineBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 97.57 | — | 15.67 | |
| MahalanobisBackbone=DenseNet-1002026.03 | 97.18 | — | 15.42 | |
| EnergyBackbone=DenseNet-1002026.03 | 96.28 | — | 14.12 | |
| MaxLogitBackbone=DenseNet-1002026.03 | 96.17 | — | 14.18 | |
| NECOBackbone=DenseNet-1002026.03 | 96.14 | — | 14.16 | |
| GODINContrastive Learning=No2026.01 | 96.1 | — | 18.72 | |
| OptFSBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 95.97 | — | 24.69 | |
| ViMBackbone=ResNet-182026.03 | 95.69 | — | 22.52 | |
| KNNContrastive Learning=No2026.01 | 95.48 | — | 27.97 | |
| ReActBackbone=DenseNet-1002026.03 | 95.43 | — | 17.44 | |
| MixOEBackbone=ResNet-182024.05 | 95.37 | — | 2,754 | |
| LaRExBackbone=ResNet-182026.03 | 95.32 | — | 18.5 | |
| NECOBackbone=ResNet-182026.03 | 95.04 | — | 32.26 | |
| DICEBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 94.96 | — | 27.75 | |
| CSIContrastive Learning=Yes2026.01 | 94.69 | — | 37.38 | |
| SITN2026.05 | 94.6 | — | — | |
| NNGuideBackbone=ResNet-182026.03 | 94.59 | — | 16.09 | |
| ProxyAnchorContrastive Learning=Yes2026.01 | 94.55 | — | 39.27 | |
| GradNormBackbone=DenseNet-1002026.03 | 94.37 | — | 34.82 | |
| MaxLogitBackbone=ResNet-182026.03 | 94.36 | — | 20.86 | |
| EnergyBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 94.19 | — | 38.79 | |
| MahalanobisBackbone=ResNet-182026.03 | 94.12 | — | 29 | |
| NNGuideBackbone=DenseNet-1002026.03 | 93.78 | — | 18.59 | |
| CSPBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 93.73 | — | 53.39 | |
| EnergyBackbone=ResNet-182026.03 | 93.69 | — | 29.2 | |
| Comp.VAEContrastive Learning=No2026.01 | 93.6 | — | 15.6 | |
| MSPBackbone=ResNet-182026.03 | 93.6 | — | 19.17 | |
| MSPBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 93.57 | — | 47.23 | |
| MSPBackbone=DenseNet-1002026.03 | 93.46 | — | 17.71 | |
| FedRoD+FOOGDalpha=0.12024.10 | 92.8 | — | 17.61 | |
| GradNormBackbone=ResNet-182026.03 | 92.76 | — | 37.65 | |
| ReActBackbone=ResNet-182026.03 | 91.64 | — | 46.31 | |
| ODINContrastive Learning=No2026.01 | 91.3 | — | 53.78 | |
| MSPContrastive Learning=No2026.01 | 91.25 | — | 59.66 | |
| EnergyContrastive Learning=No2026.01 | 91.22 | — | 54.41 | |
| NegLabelBackbone=CLIP-B/16, Method Category=Vision Language Model based Methods2026.04 | 90.91 | — | 48.17 | |
| FedRoDalpha=0.12024.10 | 90.16 | — | 40.72 | |
| FOSTERalpha=0.12024.10 | 85.95 | — | 53.24 | |
| FedTHEalpha=0.12024.10 | 83.5 | — | 48.09 | |
| Comp.VAE (vMF)Contrastive Learning=No2026.01 | 81.3 | — | 51.8 | |
| FedAvg+FOOGDalpha=0.12024.10 | 79.89 | — | 44.59 | |
| Complexity2026.05 | 78.2 | — | — | |
| FedAvgalpha=0.12024.10 | 77.02 | — | 66.41 | |
| FedATOLalpha=0.12024.10 | 70.95 | — | 61.01 | |
| KNN*Contrastive Learning=No2026.01 | 66.4 | — | 91 | |
| FedIIRalpha=0.12024.10 | 66.23 | — | 78.34 | |
| ReActBackbone=ResNet50, Method Category=Vision Model based Methods2026.04 | 66.05 | — | 97.18 | |
| FedLNalpha=0.12024.10 | 58.44 | — | 80.02 | |
| FedICONalpha=0.12024.10 | 39.22 | — | 91.75 | |
| WAICensemble size=52026.05 | 33.2 | — | — | |
| Log-likelihood2026.05 | 10.5 | — | — |