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High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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Updated: Jul 1, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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ARPruning:基于注意力图排名的自动道修剪.

Tongtong Yuan1, Zulin Li1, Bo Liu1

  • 1Beijing University of Technology, China.

Neural networks : the official journal of the International Neural Network Society
|March 6, 2024
PubMed
概括
此摘要是机器生成的。

ARPruning引入了一种新的压缩卷积神经网络 (CNN) 的方法,通过使用注意力图来排名通道重要性. 这种方法实现了高压缩率和精度,优于现有的结构化修剪技术.

关键词:
图像的分类图像的分类.模型的压缩压缩.修剪的标准 修剪的标准搜索算法 搜索算法 搜索算法

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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:

  • 结构化的修剪是压缩卷积神经网络 (CNN) 的关键.
  • 现有的方法往往缺乏可解释性,并且只依赖重量大小而导致低于最佳的压缩.
  • 确定各层的最佳修剪比率需要复杂的搜索策略.

研究的目的:

  • 为CNNs开发一种可解释且有效的结构化修剪方法.
  • 引入一个新的标准来评估使用注意力地图的内层道重要性.
  • 设计一个高效的搜索算法,以获得最佳的层间修剪比率.

主要方法:

  • ARPruning使用注意力图来创建一个可解释的层内部道重要性标准.
  • 开发了一个本地社区搜索算法,以确定最佳的层间修剪比率.
  • 该方法将修剪标准与自动搜索策略集成在一起,以实现高效的优化.

主要成果:

  • 在保持出色的模型精度的同时,ARPruning实现了高压缩率.
  • 实验结果表明,与最先进的修剪方法相比,压缩性能优越.
  • 拟议的方法为结构化修剪提供了更易于解释的方法.

结论:

  • ARPruning为CNN模型压缩提供了一个有效和可解释的解决方案.
  • 基于注意地图的标准和搜索策略显著提高了修剪效率和性能.
  • 这项工作通过提供强大而准确的结构化修剪技术,推动了模型压缩领域的发展.