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ResearchFormerly WizWand

Image Classification on ImageNet (T=250 Accuracy)

74.25Accuracy (T=250)Burst+LIPooling
60.969264.417167.86571.3129Apr 28, 2026
Updated 6mo ago

Evaluation results

MethodLinks
Burst+LIPoolingType=ANN2SNN, Architecture=VGG-16, Timesteps=T=12026.0474.25
QB-LIFType=SNN Training, Architecture=ResNet-34, Timesteps=T=42026.0473.14
Adaptive CalibrationType=ANN2SNN, Architecture=ResNet-34, Timesteps=T=12026.0472.96
QB-LIFType=SNN Training, Architecture=ResNet-34, Timesteps=T=22026.0472.31
QB-LIFType=SNN Training, Architecture=ResNet-34, Timesteps=T=12026.0471.52
Ternary SpikeType=SNN Training, Architecture=ResNet-34, Timesteps=T=42026.0470.74
FSTA-SNNType=SNN Training, Architecture=ResNet-34, Timesteps=T=42026.0470.23
QB-LIFType=SNN Training, Architecture=ResNet-18, Timesteps=T=42026.0469.89
QB-LIFType=SNN Training, Architecture=ResNet-18, Timesteps=T=22026.0469.35
QB-LIFType=SNN Training, Architecture=ResNet-18, Timesteps=T=12026.0468.92
RMP-SNNType=ANN2SNN, Architecture=ResNet-34, Timesteps=T=12026.0468.89
GLIFType=SNN Training, Architecture=ResNet-34, Timesteps=T=42026.0467.52
RecDis-SNNType=SNN Training, Architecture=ResNet-34, Timesteps=T=42026.0467.33
Real SpikeType=SNN Training, Architecture=ResNet-18, Timesteps=T=42026.0463.68
SEWType=SNN Training, Architecture=ResNet-18, Timesteps=T=42026.0463.18
MPBNType=SNN Training, Architecture=ResNet-18, Timesteps=T=42026.0463.14
RMP-LossType=SNN Training, Architecture=ResNet-18, Timesteps=T=42026.0463.03
Hybrid TrainingType=ANN2SNN, Architecture=ResNet-34, Timesteps=T=12026.0461.48