Image Classification on CIFAR-100N Fine
73.79AccuracyProMix
Evaluation results
| Method | Links | |
|---|---|---|
| ProMixBackbone=VGG-19 with BN2022.07 | 73.79 | |
| DivideMixBackbone=VGG-19 with BN2022.07 | 71.13 | |
| PES(Semi)Backbone=VGG-19 with BN2022.07 | 70.36 | |
| SOP+Backbone=VGG-19 with BN2022.07 | 67.81 | |
| ELR+Backbone=VGG-19 with BN2022.07 | 66.72 | |
| Co-Teaching+Backbone=VGG-19 with BN2022.07 | 60.37 | |
| JoCoRBackbone=VGG-19 with BN2022.07 | 59.97 | |
| NLSHyperparameter Selection=Best2021.06 | 58.59 | |
| PL2021.06 | 57.59 | |
| BLC2021.06 | 57.14 | |
| F-div2021.06 | 57.1 | |
| FLC2021.06 | 57.01 | |
| LSHyperparameter Selection=Best2021.06 | 55.84 | |
| CORES*Backbone=VGG-19 with BN2022.07 | 55.72 | |
| CE2021.06 | 55.5 | |
| CEBackbone=VGG-19 with BN2022.07 | 55.5 |