Image Classification on CIFAR-10 (test) (NLL vs Sample Size)
0.36NLL (N=100)DE ELBO
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| DE ELBOBackbone=ViT-B/16, Model=L2-SP2025.02 | 0.36 | 0.24 | 0.12 | 0.07 | |
| MAP + GSBackbone=ViT-B/16, Model=L2-SP2025.02 | 0.46 | 0.25 | 0.1 | 0.06 | |
| MAP + GSBackbone=ConNeXt-Tiny, Model=L2-SP2025.02 | 0.46 | 0.22 | 0.11 | 0.07 | |
| DE ELBOBackbone=ConNeXt-Tiny, Model=L2-SP2025.02 | 0.54 | 0.23 | 0.12 | 0.07 | |
| MAP + GSBackbone=ResNet-50, Model=PTYL2025.02 | 0.9 | 0.36 | 0.15 | 0.1 | |
| MAP + LaplaceBackbone=ResNet-50, Model=L2-SP2025.02 | 0.94 | 0.4 | 0.15 | 0.09 | |
| MAP + LaplaceBackbone=ResNet-50, Model=L2-zero2025.02 | 0.97 | 0.41 | 0.19 | 0.1 | |
| DE ELBOBackbone=ResNet-50, Model=PTYL, Order=22025.02 | 1 | 0.46 | 0.23 | 0.12 | |
| MAP + GSBackbone=ResNet-50, Model=PTYL (SSL)2025.02 | 1.01 | 0.44 | 0.24 | 0.12 | |
| DE ELBOBackbone=ResNet-50, Model=L2-zero2025.02 | 1.05 | 0.46 | 0.2 | 0.12 | |
| MAP + GSBackbone=ResNet-50, Model=Linear probing2025.02 | 1.22 | 0.75 | 0.55 | 0.48 | |
| MAP + LaplaceBackbone=ResNet-50, Model=PTYL2025.02 | 1.34 | 0.5 | 0.23 | 0.11 | |
| DE ELBOBackbone=ResNet-50, Model=PTYL, Order=12025.02 | 1.36 | 0.76 | 0.29 | 0.12 | |
| MAP + GSBackbone=ResNet-50, Model=L2-zero2025.02 | 1.89 | 1.06 | 0.55 | 0.48 | |
| DE ELBOBackbone=ResNet-50, Model=L2-SP2025.02 | 2.3 | 1.18 | 0.78 | 0.18 | |
| MAP + GSBackbone=ResNet-50, Model=L2-SP2025.02 | 2.76 | 0.54 | 0.39 | 0.28 |