ACLI:一个CNN修剪框架,利用相邻的卷积层相互依赖性和 $\gamma$γ-Weakly Submodularity
IEEE transactions on pattern analysis and machine intelligence
|September 16, 2025
概括
本研究引入了使用马弱子模块化进行卷积神经网络 (CNN) 修剪的新理论框架. 拟议的无数据算法有效地减少了网络参数,同时提高了准确性和资源效率.
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:
- 目前的卷积神经网络 (CNN) 修剪方法通常依赖于手动启发式,限制其通用性和性能.
- 现有的技术可能缺乏稳定性和保证性能,因为它们的启发性质.
研究的目的:
- 提出一种新的理论框架,用于使用马弱子模块化的CNN修剪.
- 开发一种无数据,低复杂度的算法,用于卷积层中的过器选择.
主要方法:
- 利用马弱子模块化与一个新的重要性函数来自错误界限.
- 制定波器的重要性作为一个马弱子模块函数.
- 开发一种无数据的无意识算法,用于过器修剪.
主要成果:
- 拟议的方法在数据集中超越了最先进的网络,达到76.52%的准确性.
- 网络参数减少了25.5%,并且具有竞争力的准确性.
- 与基线相比,ACLI方法显示了数量级更高的资源效率.
结论:
- 马弱子模块化框架为CNN修剪提供了一种有效和高效的方法.
- 开发的算法提供了一个无数据,低复杂度的解决方案,具有卓越的资源效率和准确性.
- 这种方法代表了优化CNN用于实际应用的重大进步.
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