较少的尖端活动可能更好: 功能 精制和掩饰尖端神经网络用于基于事件的视觉识别
Man Yao1, Hengyu Zhang2, Guangshe Zhao3
1School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Peng Cheng Laboratory, Shenzhen 518000, China.
概括
本研究介绍了精制和掩饰尖端神经网络 (RM-SNN),这是一个基于事件的视觉系统,可以适应稀疏和不均的事件流. RM-SNN通过改进和掩盖功能来提高效率和性能,以减少不必要的尖端活动.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 基于事件的视觉提供高时间分辨率和动态感知,适合专门的视觉任务.
- 尖端神经网络 (SNN) 非常适合事件流处理,因为它们的时间性和事件驱动性.
- 现有的SNN与事件数据的空间稀疏性和时间不均性作斗争,影响效率.
研究的目的:
- 开发一个适应性的SNN,有效地处理稀疏和时间变化的基于事件的视觉数据.
- 通过优化它们对事件流的响应来提高SNNs的效率和性能.
- 在SNN中引入一种新的模块,用于在SNN中提炼和掩盖特征.
主要方法:
- 提出了精炼和掩饰尖端神经网络 (RM-SNN) 架构.
- 引入了精细化和掩盖 (RM) 模块,以改进特征并掩盖不重要的特征,优化神经元膜潜力.
- 在时间和通道维度应用RM模块来处理时空事件数据.
主要成果:
- 在多个基于事件的基准指标中,RM-SNN显著降低了平均升活动率.
- 提出的方法改善了包括DVS128 Gesture,CIFAR10-DVS和UCF101-DVS在内的数据集的任务性能.
- 实验表明,较少的尖端活动与更好的网络性能相关.
结论:
- RM-SNN有效地解决了处理稀疏和不统一的基于事件的数据的挑战.
- 功能改进和掩盖方法优化SNN,提高效率和准确性.
- 这些发现突出了基于事件视觉的适应性,数据驱动的尖端调节的好处.
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