时间单尖编码,用于在尖神经网络中有效的转移学习
Hamideh Moqadasi1,2, Saeed Safari3, Fernando Mateo4
1School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. h.moqadasi@ut.ac.ir.
Scientific reports
|October 1, 2025
概括
有效转移学习 (TS4TL) 的时间单编码引入了对尖端神经网络 (SNN) 的高效监督学习规则. 这种方法通过使用"绝对目标"策略来增强转移学习,减少培训时间和能源消耗.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 尖端神经网络 (SNN) 提供节能计算,但面临培训挑战.
- 转移学习 (TL) 有效地利用预训练模型,但需要有效地与SNNs集成.
- 在SNN中,时间编码,特别是单编码,为高效的学习规则提供了机会.
研究的目的:
- 引入一种新型的监督学习规则,即有效转移学习的临时单编码 (TS4TL),用于训练多层完全连接的SNNs.
- 为单尖时间编码提出"绝对目标"分配方法,以简化和加快SNN培训.
- 为了证明TS4TL在转移学习框架中的有效性,用于分类任务,特别是有限的数据.
主要方法:
- 开发了TS4TL,这是一个监督学习规则,集成了单尖时代编码的"绝对目标"方法.
- 实施了TS4TL,用于训练SNN作为转移学习管道中的分类器块.
- 在基准数据集上评估了TS4TL,包括Eth80,时尚-MNIST,MNIST和Caltech101-Face/Bike.
主要成果:
- 实现了最先进的精度:98.91%在Eth80,91.89%在时尚-MNIST上,98.45%在MNIST上,以及97.75%在Caltech101-Face/Bike.上.
- 与现有方法相比,证明了较低的计算复杂性,培训时间和能源消耗.
- 成功地减少了神经元失火,确保了准确的第一个尖峰编码和稳定的训练.
结论:
- TS4TL提供了一个可扩展,高效和高性能解决方案,用于SNNs的时间学习.
- "绝对目标"方法简化了培训,同时保持了准确性和减少了对资源的需求.
- TS4TL有效地利用转移学习进行SNN分类,即使数据分布有限或多样化.
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