Image Classification on MNIST Label Noise 0.2 (test)
94.6Mean AccuracyGIW
Evaluation Results
| Method | Links | |
|---|---|---|
| GIWShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 94.6 | |
| GIWShift Scenario=Case (iv)2023.05 | 91.44 | |
| ReweightShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 89.28 | |
| CCSAShift Scenario=Case (iv)2023.05 | 88.06 | |
| Pretrain-valShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 86.34 | |
| DANNShift Scenario=Case (iv)2023.05 | 85.32 | |
| Pretrain-valShift Scenario=Case (iv)2023.05 | 85.08 | |
| R-DIWShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 82.7 | |
| MW-NetShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 82.29 | |
| DIWShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 80.12 | |
| Val-onlyShift Scenario=Case (iv)2023.05 | 79.86 | |
| Val-onlyShift type=Label Noise, Shift intensity=0.2, Case=iii, Trials=52023.05 | 78.94 |