Image Classification on CIFAR-10-LT λ=100 (test)
19.46Error RateMetaSAug with cross-entropy loss
Evaluation Results
| Method | Links | |
|---|---|---|
| MetaSAug with cross-entropy lossBackbone=ResNet-322021.12 | 19.46 | |
| Bag of TricksBackbone=ResNet-322021.12 | 19.97 | |
| RISDABackbone=ResNet-322021.12 | 20.11 | |
| BBNBackbone=ResNet-322021.12 | 20.18 | |
| Hybrid-PSCBackbone=ResNet-322021.12 | 21.18 | |
| Meta-class-weight with cross-entropy lossBackbone=ResNet-322021.12 | 23.59 | |
| GCEModel=Wide-ResNet2026.04 | 23.75 | |
| GCEModel=DenseNet1212026.04 | 23.79 | |
| MMCEModel=Wide-ResNet2026.04 | 25.01 | |
| CEModel=Wide-ResNet2026.04 | 25.2 | |
| Brier LossModel=DenseNet1212026.04 | 25.3 | |
| GCEModel=ResNet-502026.04 | 25.35 | |
| GCEModel=ResNet-1102026.04 | 25.62 | |
| DFLModel=Wide-ResNet2026.04 | 25.63 | |
| CEModel=DenseNet1212026.04 | 26.02 | |
| MMCEModel=DenseNet1212026.04 | 26.07 | |
| DFLModel=DenseNet1212026.04 | 26.21 | |
| Meta-weight netBackbone=ResNet-322021.12 | 26.43 | |
| CEModel=ResNet-502026.04 | 26.62 | |
| MMCEModel=ResNet-502026.04 | 26.69 | |
| MMCEModel=ResNet-1102026.04 | 26.77 | |
| FLSDModel=DenseNet1212026.04 | 26.84 | |
| CEModel=ResNet-1102026.04 | 26.85 | |
| FLSDModel=Wide-ResNet2026.04 | 26.88 | |
| DFLModel=ResNet-502026.04 | 26.89 | |
| MixupBackbone=ResNet-322021.12 | 26.94 | |
| DFLModel=ResNet-1102026.04 | 27 | |
| Class-balanced CEBackbone=ResNet-322021.12 | 27.32 | |
| Brier LossModel=Wide-ResNet2026.04 | 27.58 | |
| L2RWBackbone=ResNet-322021.12 | 27.77 | |
| FLSDModel=ResNet-1102026.04 | 28.16 | |
| FLSDModel=ResNet-502026.04 | 28.43 | |
| Class-balanced fine-tuningBackbone=ResNet-322021.12 | 28.66 | |
| AFLModel=Wide-ResNet2026.04 | 28.79 | |
| AFLModel=DenseNet1212026.04 | 28.92 | |
| AFLModel=ResNet-1102026.04 | 29.24 | |
| Cross-EntropyBackbone=ResNet-322021.12 | 29.86 | |
| AFLModel=ResNet-502026.04 | 29.92 | |
| Brier LossModel=ResNet-1102026.04 | 30.82 | |
| Brier LossModel=ResNet-502026.04 | 31.05 |