Research map / Architectures and efficiency / Spiking networks
Direct Training for Spiking Neural Networks: Faster, Larger, Better
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add NeuNorm (ablation) | Directly trained SNN · N-MNIST | accuracy: 99.44% -> 99.53% | Helped |
| Add NeuNorm (ablation) | Directly trained SNN · DVS-CIFAR10 | accuracy: 58.1% -> 60.5% | Helped |
| Add NeuNorm (ablation) | Directly trained SNN · DVS-CIFAR10 | training epochs to best accuracy: 157 -> 103 | Helped |
| Add NeuNorm (ablation) | Directly trained SNN · CIFAR10 | accuracy: 89.83% -> 90.53% | Helped |
| Directly train an SNN instead of using the reported prior direct-training method | CIFAR10 · CIFAR10 | accuracy: 75.42% -> 90.53% | Helped |
| Use direct SNN training with the reported AlexNet-like structure | AlexNet-like SNN · CIFAR10, AlexNet-like structure | accuracy: 83.72% -> 85.24% | Helped |
Closest papers by meaning
- Going Deeper With Directly-Trained Larger Spiking Neural Networks (AAAI 2021 · similarity 0.94)
- Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance (AAAI 2021 · similarity 0.93)
- Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training (NeurIPS 2025 · similarity 0.92)
- Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips (ICLR 2024 · similarity 0.91)
- Enhancing Training of Spiking Neural Network with Stochastic Latency (AAAI 2024 · similarity 0.91)
- EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature (NeurIPS 2024 · similarity 0.91)
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