Image Classification on CIFAR-10-N
95.9Accuracy (20)Dynamic Loss
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| Dynamic LossBackbone=PARes182022.11 | 95.9 | 94.69 | 92.28 | 95.74 | 94.51 | 94.62 | |
| DivideMixBackbone=PARes182022.11 | 95.63 | 93.78 | 94.23 | 94.18 | 92.73 | 94.11 | |
| NCRBackbone=PARes182022.11 | 95.2 | 94.5 | 78.45 | — | 90.7 | — | |
| NCTBackbone=PARes182022.11 | 95 | 87 | 73.22 | 91.51 | 93 | 87.95 | |
| GJSBackbone=PARes182022.11 | 94.2 | 92.8 | 89.72 | 91.92 | 86.07 | 90.94 | |
| MOIT+Backbone=PARes182022.11 | 94.08 | 91.95 | 89.38 | 94.5 | 93.27 | 92.64 | |
| CoteachingBackbone=PARes182022.11 | 93.83 | 91.74 | 57.65 | 93.23 | 90.78 | 85.45 | |
| CMW-NetBackbone=PARes182022.11 | 91.09 | 86.91 | 83.33 | 93.02 | 92.7 | 89.41 | |
| Cross EntropyBackbone=PARes182022.11 | 86.98 | 77.52 | 73.63 | 83.6 | 77.85 | 79.92 | |
| PLCBackbone=PARes182022.11 | 86.4 | 71.72 | 65.22 | 90.23 | 85.4 | 79.79 | |
| SELFIEBackbone=PARes182022.11 | 86.39 | 82.23 | 74.81 | — | — | — |