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Updated: Jul 8, 2025

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关于使用l0-norm规范化和重量修剪来压缩神经网络
Felipe Dennis de Resende Oliveira1, Eduardo Luiz Ortiz Batista1, Rui Seara1
1LINSE-Circuits and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, 88040-900, Brazil.
概括
这项研究引入了一种新的神经网络压缩方法,使用L0-规范规范化和修剪. 该技术有效地减少了网络尺寸和部署成本,同时保持了边缘智能应用的高精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 实施的复杂性和高成本阻碍了神经网络的部署,特别是边缘智能和嵌入式系统.
- 网络压缩技术对于降低部署成本,同时保持推断准确度至关重要.
研究的目的:
- 开发一种新的神经网络压缩方案.
- 解决在资源有限的环境中部署复杂神经网络所面临的挑战.
主要方法:
- 开发了一种基于L0规范的新型规范化,以在训练期间诱导网络稀疏性.
- 应用了修剪技术,以从训练网络中去除较小的重量.
- 整合了L2规范规范化,以防止过度装配和微调以提高性能.
主要成果:
- 拟议的压缩方案成功创建了更小,高效的神经网络.
- 实验结果证明了新型压缩方法的有效性.
- 与竞争方法的比较凸显了拟议方案的优势.
结论:
- 新的压缩方案为在边缘设备上部署高效的神经网络提供了可行的解决方案.
- 该方法平衡了网络尺寸的减少,同时保持了令人满意的推断准确性.
- 这项研究有助于在实际应用中推进高效的深度学习模型.
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