# Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks

arXiv: https://arxiv.org/abs/2503.03144 · HTML: https://mlsanity.com/paper/2503.03144/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for spiking neural networks, one of 12 directions in Knowledge distillation.
- Builds on 33 of its 70 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Placed in

- Knowledge distillation / [Distillation for spiking neural networks](https://mlsanity.com/distill/distillation-for-spiking-neural-networks/)

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Modeling the Background for Incremental Learning in Semantic Segmentation](https://mlsanity.com/paper/2002.00718/) (2020)
- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (2021)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [Temporal-wise Attention Spiking Neural Networks for Event Streams Classification](https://mlsanity.com/paper/2107.11711/) (2021)
- [HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise](https://mlsanity.com/paper/2110.11417/) (2021)
- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2304.05627/) (2023)
- [Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization](https://mlsanity.com/paper/2305.02099/) (2023)
- [Membrane Potential Batch Normalization for Spiking Neural Networks](https://mlsanity.com/paper/2308.08359/) (2023)
- [Distilling Spikes: Knowledge Distillation in Spiking Neural Networks](https://mlsanity.com/paper/2005.00288/) (2020)
- [LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.09101/) (2023)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks](https://mlsanity.com/paper/2304.09500/) (2023)

## Built on by (most cited first, 10 of 10)

- [Low-bit Model Quantization for Deep Neural Networks: A Survey](https://mlsanity.com/paper/2505.05530/) (2025)
- [TS-SNN: Temporal Shift Module for Spiking Neural Networks](https://mlsanity.com/paper/2505.04165/) (2025)
- [Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models](https://mlsanity.com/paper/2601.20687/) (2026)
- [ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks](https://mlsanity.com/paper/2506.07720/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2504.20445/) (2025)
- [Temporal Regularization Training: Unleashing the Potential of Spiking Neural Networks](https://mlsanity.com/paper/2506.19256/) (2025)
- [STAER: Temporal Aligned Rehearsal for Continual Spiking Neural Network](https://mlsanity.com/paper/2601.20870/) (2026)
- [Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2605.14252/) (2026)
- [STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation](https://mlsanity.com/paper/2605.27409/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a performance gap persists between SNNs and Artificial Neural Networks (ANNs), primarily due to current training methods and inherent model limitations. While recent research has aimed to enhance SNN learning by employing knowledge distillation (KD) from ANN teacher networks, traditional distillation techniques often overlook the distinctive spatiotemporal properties of SNNs, thus failing to fully leverage their advantages. To overcome these challenge, we propose a novel logit distillation method characterized by temporal separation and entropy regularization. This approach improves existing SNN distillation techniques by performing distillation learning on logits across different time steps, rather than merely on aggregated output features. Furthermore, the integration of entropy regularization stabilizes model optimization and further boosts the performance. Extensive experimental results indicate that our method surpasses prior SNN distillation strategies, whether based on logit distillation, feature distillation, or a combination of both. The code will be available on GitHub.