Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networks

arXiv:2604.25688 · cs.CV · Submitted 2026-04-28 · Read on arXiv

cs.CV

Submitted: 2026-04-28

Updated: 2026-09-16

Code: https://github.com/hzc1208/LMHT_SNN

Terminology

• Quantization — 52× in this paper · explained in 12 episodes
• Spiking neural networks — 35× in this paper · explained in 2 episodes
• Accuracy — 26× in this paper · explained in 1 episode
• Ablation — 10× in this paper · explained in 1 episode
• Learnable Burst Quantization — 10× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Spiking Neural Networks (SNNs) — 5× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• European Conference on Computer Vision — 3× in this paper
• Sparsity — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• International Conference on Learning Representations — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• International Joint Conference on Neural Networks — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Thirty-First International Joint Conference on Artificial Intelligence — 1× in this paper

Sources

• Improved Regularization of Convolutional Neural Networks with Cutout
• Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

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