Image Classification on CIFAR-10 (Symmetry/Asymmetry Accuracy)
94.37Accuracy (Test, symm 20%)CAR
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| CARBackbone=ResNet34, Learning rate scheduler=cosine annealing, Trials=32021.08 | 94.37 | 93.49 | 90.56 | 80.98 | 92.09 | |
| SATBackbone=ResNet34, Learning rate scheduler=cosine annealing, Trials=32021.08 | 94.14 | 92.64 | 89.23 | 78.58 | 91.43 | |
| DACBackbone=ResNet34, Trials=32021.08 | 92.91 | 90.71 | 86.3 | 74.84 | 91.54 | |
| Joint OptBackbone=ResNet34, Trials=32021.08 | 92.25 | 90.79 | 86.87 | 69.16 | 91.21 | |
| ELRBackbone=ResNet34, Learning rate scheduler=cosine annealing, Trials=32021.08 | 92.12 | 91.43 | 88.87 | 80.69 | 90.35 | |
| SELFBackbone=ResNet34, Trials=32021.08 | 91.13 | 90.3 | 86.4 | 63.59 | 88.32 | |
| GCEBackbone=ResNet34, Trials=32021.08 | 89.83 | 87.13 | 82.54 | 64.07 | 85.21 | |
| SLBackbone=ResNet34, Trials=32021.08 | 89.83 | 87.13 | 82.81 | 68.12 | 81.55 | |
| Forward TBackbone=ResNet34, Trials=32021.08 | 87.99 | 83.25 | 74.96 | 54.64 | 86.44 | |
| NLNLBackbone=ResNet34, Trials=32021.08 | 87.4 | 81.25 | 68.3 | 39.54 | 81.53 | |
| Cross EntropyBackbone=ResNet34, Trials=32021.08 | 86.98 | 81.88 | 74.14 | 53.82 | 85.54 | |
| BootstrapBackbone=ResNet34, Trials=32021.08 | 86.23 | 82.23 | 75.12 | 54.12 | 85.11 |