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Updated: Jun 29, 2025

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SGLFormer:具有高性能的全球-本地融合变压器

Han Zhang1,2, Chenlin Zhou1, Liutao Yu1

  • 1AI Department, Peng Cheng Laboratory, Shenzhen, China.

Frontiers in neuroscience
|March 29, 2024
PubMed
概括

本研究介绍了Spiking全球局部融合变压器 (SGLFormer),这是一个新的Spiking神经网络架构,可以显著提高计算机视觉任务的性能. 通过整合变压器和卷积结构,改善梯度反向传播,SGLFormer实现了最先进的结果.

关键词:
全球-本地-融合.最多的共享服务 (Maxpooling)尖神经网络的神经网络高性能的高性能性能.时间空间的时间空间.尖尖的变压器的使用.

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