Image Classification on MNIST Label Noise 0.4 (test)
93.74Mean AccuracyGIW
Evaluation Results
| Method | Links | |
|---|---|---|
| GIWShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 93.74 | |
| GIWShift Scenario=Case (iv)2023.05 | 88.84 | |
| Pretrain-valShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 85.43 | |
| ReweightShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 84.73 | |
| CCSAShift Scenario=Case (iv)2023.05 | 84 | |
| Pretrain-valShift Scenario=Case (iv)2023.05 | 83.26 | |
| R-DIWShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 81.19 | |
| MW-NetShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 80.92 | |
| Val-onlyShift Scenario=Case (iv)2023.05 | 79.86 | |
| DIWShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 79 | |
| Val-onlyShift type=Label Noise, Shift intensity=0.4, Case=iii, Trials=52023.05 | 78.94 | |
| DANNShift Scenario=Case (iv)2023.05 | 58.02 |