Out-of-Distribution Detection on CIFAR100 (ID) SVHN (OOD)
99.87AUROCMedix
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| MedixBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 99.87 | 0.48 | — | — | |
| Prototype FusionBackbone=DenseNet-1002026.03 | 96.33 | — | — | 17.91 | |
| GramBackbone=ResNet182024.05 | 95.55 | 20.06 | — | — | |
| Prototype FusionBackbone=ResNet-182026.03 | 95.52 | — | — | 22.31 | |
| CIDERBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 95.16 | 23.09 | — | — | |
| ESOODBackbone=DenseNet-1002026.03 | 94.56 | — | — | 30.32 | |
| SSD+Backbone=ResNet-34, Performance averaged over five runs=true2025.10 | 94.19 | 31.19 | — | — | |
| DRLBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 94.07 | 20.15 | — | — | |
| ViMBackbone=DenseNet-1002026.03 | 93.66 | — | — | 28.37 | |
| LaRExBackbone=DenseNet-1002026.03 | 93.49 | — | — | 29.98 | |
| KNN+Backbone=ResNet-34, Performance averaged over five runs=true2025.10 | 92.78 | 39.23 | — | — | |
| CSIBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 92.65 | 44.53 | — | — | |
| NACBackbone=ResNet182024.05 | 92.4 | 24.39 | — | — | |
| LaRExBackbone=ResNet-182026.03 | 90.45 | — | — | 36.09 | |
| CONJBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 90.44 | 46.19 | — | — | |
| KNNBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 90.39 | 46.25 | — | — | |
| ESOODBackbone=ResNet-182026.03 | 90.19 | — | — | 35.45 | |
| ASHBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 90.19 | 52.96 | — | — | |
| MahalanobisBackbone=DenseNet-1002026.03 | 89.23 | — | — | 43.25 | |
| ReActBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 88.75 | 50.93 | — | — | |
| ReActBackbone=DenseNet-1002026.03 | 85.63 | — | — | 41.7 | |
| ASHBackbone=ResNet182024.05 | 85.6 | 46 | — | — | |
| EnergyBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 85.25 | 66.91 | — | — | |
| ODINBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 84.88 | 70.16 | — | — | |
| VimBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 84.62 | 73.42 | — | — | |
| NECOBackbone=DenseNet-1002026.03 | 83.85 | — | — | 47.19 | |
| MaxLogitBackbone=DenseNet-1002026.03 | 83.45 | — | — | 48.09 | |
| EnergyBackbone=DenseNet-1002026.03 | 83.44 | — | — | 48.1 | |
| NNGuideBackbone=DenseNet-1002026.03 | 83.04 | — | — | 41.86 | |
| GradNormBackbone=DenseNet-1002026.03 | 82.86 | — | — | 64.1 | |
| VOSBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 82.8 | 43.24 | — | — | |
| ProxyAnchorBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 82.43 | 87.21 | — | — | |
| Entropy-MCMC2025.10 | 81.14 | — | 87.18 | — | |
| WeiPer+MSPBackbone=ResNet182024.05 | 80.9 | 59.31 | — | — | |
| MahalanobisBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 80.62 | 87.09 | — | — | |
| fSGLD2025.10 | 80.52 | — | 88.01 | — | |
| ASAM2025.10 | 79.86 | — | 87.93 | — | |
| MSPBackbone=DenseNet-1002026.03 | 79.82 | — | — | 52.74 | |
| MSPBackbone=ResNet-34, Performance averaged over five runs=true2025.10 | 79.8 | 78.89 | — | — | |
| Entropy-SGD2025.10 | 79.15 | — | 86.92 | — | |
| MahalanobisBackbone=ResNet-182026.03 | 77.92 | — | — | 61.76 | |
| ViMBackbone=ResNet-182026.03 | 76.72 | — | — | 68.15 | |
| NNGuideBackbone=ResNet-182026.03 | 76.51 | — | — | 68.25 | |
| SAM2025.10 | 74.56 | — | 84.61 | — | |
| MSPBackbone=ResNet-182026.03 | 72.92 | — | — | 83.01 | |
| SGLD2025.10 | 72.51 | — | 83.35 | — | |
| SGD2025.10 | 71.96 | — | 84.08 | — | |
| Entropy-SGLD2025.10 | 71.83 | — | 82.89 | — | |
| MaxLogitBackbone=ResNet-182026.03 | 67.27 | — | — | 90.25 | |
| EnergyBackbone=ResNet-182026.03 | 58.26 | — | — | 91.68 | |
| NECOBackbone=ResNet-182026.03 | 45.93 | — | — | 91.06 | |
| ReActBackbone=ResNet-182026.03 | 36.6 | — | — | 92.76 | |
| GradNormBackbone=ResNet-182026.03 | 22.41 | — | — | 98.05 | |
| DAEDL2025.10 | — | — | 72.07 | — | |
| Dropout2025.10 | — | — | 71.83 | — | |
| EDL2025.10 | — | — | 56.21 | — | |
| F-EDL2025.10 | — | — | 75.35 | — | |
| I-EDL2025.10 | — | — | 67.51 | — | |
| R-EDL2025.10 | — | — | 61.8 | — |