Image Classification on CIFAR-100N 1.0 (test)
62.34Accuracy (Noisy)GNL - p(X,Y)
Evaluation Results
| Method | Links | |
|---|---|---|
| GNL - p(X,Y)Backbone=ResNet-34, Classifier Type=single classifier2023.08 | 62.34 | |
| CALBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 61.73 | |
| CORES^2Backbone=ResNet-34, Classifier Type=single classifier2023.08 | 61.15 | |
| GNL - p(Y|X)Backbone=ResNet-34, Classifier Type=single classifier2023.08 | 59.03 | |
| Negative-LSBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 58.59 | |
| VolMinNetBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 57.8 | |
| Peer LossBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 57.59 | |
| F-DivBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 57.1 | |
| Forward TBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 57.01 | |
| Positive-LSBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 55.84 | |
| CEBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 55.5 | |
| T-RevisionBackbone=ResNet-34, Classifier Type=single classifier2023.08 | 51.55 |