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Updated: Jul 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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RASP:基于规范化的振幅突出性修剪.

Chenghui Zhen¹, Weiwei Zhang², Jian Mo²

¹College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, Fujian, China.

Neural networks : the official journal of the International Neural Network Society

|September 21, 2023

概括

本研究介绍了一种数据独立的过器修剪方法 (RASP),用于高效的深度神经网络. 拉斯普提高模型准确性,降低计算成本,使其成为资源有限的设备的理想选择.

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:

现有的过器修剪方法通常依赖于数据依赖的标准,这限制了它们的理论可行性和在资源有限的设备上实际部署.
基于振幅测量的规范标准在理论基础和过器重要性定量分析方面面临挑战.
当前修剪标准中使用的数据衍生信息阻碍了真正的数据独立性和可靠的评估.

关键词:

过器的修剪过器的修剪模型的压缩压缩.修剪的标准修剪的标准规范化规范化规范化

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