Image Classification on CIFAR10 (test) (ANN & SNN Accuracy)
95.17ANN AccuracyKDSNN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| KDSNNANN Architecture=Pyramidnet18, SNN Architecture=VGG16, timestep=42023.04 | 95.17 | 91.05 | |
| KDSNNANN Architecture=Pyramidnet18, SNN Architecture=Resnet18, timestep=42023.04 | 95.17 | 93.41 | |
| RMPANN Architecture=VGG16, SNN Architecture=VGG16, timestep=20482023.04 | 93.63 | 93.63 | |
| Hybrid TrainANN Architecture=Resnet20, SNN Architecture=Resnet20, timestep=2502023.04 | 93.15 | 92.22 | |
| Hybrid TrainANN Architecture=VGG16, SNN Architecture=VGG16, timestep=1002023.04 | 92.81 | 91.13 | |
| Opt.ANN Architecture=Resnet20, SNN Architecture=Resnet20, timestep=162023.04 | 92.46 | 92.41 | |
| Opt.ANN Architecture=VGG16, SNN Architecture=VGG16, timestep=162023.04 | 92.34 | 92.29 | |
| SPIKE-NORMANN Architecture=VGG16, SNN Architecture=VGG16, timestep=25002023.04 | 91.7 | 91.55 | |
| RMPANN Architecture=Resnet20, SNN Architecture=Resnet20, timestep=20482023.04 | 91.47 | 91.36 | |
| SPIKE-NORMANN Architecture=Resnet20, SNN Architecture=Resnet20, timestep=25002023.04 | 89.1 | 87.46 |