Research map / Architectures and efficiency / Spiking networks
SLAYER: Spike Layer Error Reassignment in Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SLAYER instead of Lee et al.'s SNN learning approach | SNN classification · MNIST | accuracy: 99.31% -> 99.36 ± 0.05% | No clear effect |
| Use SLAYER instead of ANN-to-SNN conversion | SNN classification · MNIST | accuracy: 99.44% -> 99.36 ± 0.05% | Hurt |
| Use SLAYER instead of Lee et al.'s SNN learning approach | SNN classification · NMNIST | accuracy: 98.66% -> 99.20 ± 0.02% | Helped |
| Use SLAYER instead of TrueNorth | SNN action classification · DVS Gesture | accuracy: 91.77% (94.59%) -> 93.64 ± 0.49% | Mixed |
| Use SLAYER with the same encoded spike inputs instead of SOM-SNN | TIDIGITS classification · TIDIGITS | accuracy: 97.6% -> 99.09 ± 0.13% | Helped |
Closest papers by meaning
- Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks (NeurIPS 2020 · similarity 0.92)
- Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation (ICLR 2020 · similarity 0.91)
- Training Spiking Neural Networks with Event-driven Backpropagation (NeurIPS 2022 · similarity 0.90)
- Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation (NeurIPS 2024 · similarity 0.88)
- Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness (ICLR 2025 · similarity 0.87)
- EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature (NeurIPS 2024 · similarity 0.87)
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