Long Tail Learning On Cifar 10 Lt R 50
평가 지표
Error Rate
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Error Rate | Paper Title | Repository |
|---|---|---|---|
| GLMC + SAM | 8.44 | Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data | |
| DeiT-LT | 10.2 | DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets | |
| NCL(ResNet32) | 13.2 | Nested Collaborative Learning for Long-Tailed Visual Recognition | |
| MetaSAug-LDAM | 15.66 | MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition | |
| MDCS | 11.7 | MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition | |
| OPeN (WideResNet-28-10) | 10.8 | Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images | |
| ConCutMix | 12.0 | Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation | |
| SURE(ResNet-32) | 9.78 | SURE: SUrvey REcipes for building reliable and robust deep networks |
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