Hyper-data Cleaning on MNIST (test)
0.9316Test AccuracyVPBGD(+EHG)
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| VPBGD(+EHG)Architecture=Multilayer Perceptron (MLP)2026.02 | 0.9316 | — | 90.79 | |
| VPBGDArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.9181 | — | 90.18 | |
| NHGD2026.02 | 0.918 | 0.2765 | — | |
| TTSA2026.02 | 0.9172 | 0.235 | — | |
| Neumann40iterations=402026.02 | 0.917 | 0.2244 | — | |
| Neumann10iterations=102026.02 | 0.9168 | 0.2627 | — | |
| CG10iterations=102026.02 | 0.9127 | 0.188 | — | |
| OEHGArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.9124 | — | 90.89 | |
| AmIGO2026.02 | 0.9101 | 0.1925 | — | |
| CG40iterations=402026.02 | 0.9067 | 0.1488 | — | |
| stocBiO2026.02 | 0.906 | 0.4253 | — | |
| OEHG-Weight TrainingArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.9052 | — | — | |
| IPATT-GM(+EHG)Architecture=Multilayer Perceptron (MLP)2026.02 | 0.901 | — | 91.09 | |
| T-RHG-Weight TrainingArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.8962 | — | — | |
| RHG-Weight TrainingArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.8952 | — | — | |
| IPATT-GMArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.8947 | — | 90.83 | |
| SOBA2026.02 | 0.8924 | 0.1055 | — | |
| OEHGArchitecture=Softmax Regression (SR)2026.02 | 0.892 | — | 89.94 | |
| OEHG-Weight TrainingArchitecture=Softmax Regression (SR)2026.02 | 0.8905 | — | — | |
| IPATT-GM(+EHG)Architecture=Softmax Regression (SR)2026.02 | 0.8868 | — | 90.78 | |
| IPATT-GMArchitecture=Softmax Regression (SR)2026.02 | 0.8847 | — | 90.73 | |
| VPBGD(+EHG)Architecture=Softmax Regression (SR)2026.02 | 0.8759 | — | 89.86 | |
| T-RHGArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.8596 | — | 88.84 | |
| RHGArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.8591 | — | 89.3 | |
| RHG-Weight TrainingArchitecture=Softmax Regression (SR)2026.02 | 0.8514 | — | — | |
| VPBGDArchitecture=Softmax Regression (SR)2026.02 | 0.8512 | — | 88.75 | |
| T-RHG-Weight TrainingArchitecture=Softmax Regression (SR)2026.02 | 0.8505 | — | — | |
| RHGArchitecture=Softmax Regression (SR)2026.02 | 0.8483 | — | 88.45 | |
| T-RHGArchitecture=Softmax Regression (SR)2026.02 | 0.8481 | — | 88.11 | |
| Dirty TrainingArchitecture=Softmax Regression (SR)2026.02 | 0.8152 | — | — | |
| Dirty TrainingArchitecture=Multilayer Perceptron (MLP)2026.02 | 0.7421 | — | — |