Table 1.
Classification accuracy on MNIST, CIFAR10, and CIFAR100 datasets
| Models | Training method | MNIST | CIFAR10 | CIFAR100 |
|---|---|---|---|---|
| Spiking CNN44 | conversion | – | 82.95 | – |
| BackRes45 | BP | – | 84.98 | – |
| ContinueSNN46 | conversion | 99.44 | 90.85 | – |
| Spike-Norm19 | conversion | – | 91.55 | – |
| STBP31 | BP | 99.42 | 50.7 | – |
| HM2BP33 | BP | 99.49 | – | – |
| LISNN47 | BP | 99.5 | – | – |
| BNTT48 | BP | – | 90.5 | 66.6 |
| STBP NeuNorm32 | BP | – | 90.53 | |
| BackEISNN49 | BP | 99.67 | 90.93 | – |
| SBPSNN43 | BP | 99.59 | 90.95 | – |
| TSSL-BP34 | BP | 99.53 | 91.41 | – |
| ST-RSBP50 | BP | 99.62 | – | – |
| RNL51 | conversion | 99.51 | 93.45 | 75.1 |
| SNASNet-Fw 52 | NAS + BP | – | 93.64 | 70.06 |
| SNASNet-Bw 52 | NAS + BP | – | 94.12 | 73.04 |
| Our method | BP | 99.67 | 92.15 | 68.28 |
| Our method ResNet34 | BP | – | 94.51 | 69.32 |