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通过与注意力机制的渐进部分连接来搜索神经架构
Cong Jin1, Jinjie Huang2,3, Yuanjian Chen1
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
Scientific reports
|March 19, 2024
概括
本研究介绍了一种基于注意力的渐进式部分连接神经架构搜索 (PPCAtt-NAS) 方法. PPCAtt-NAS降低了计算成本,并弥合了深度神经网络中的深度差距.
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:
- 微分架构搜索 (DAS) 面临着高计算需求和深度网络中的深度差距问题.
- 现有的方法很难有效地搜索复杂的深层神经网络架构.
研究的目的:
- 提出一种基于注意力的渐进式部分连接神经架构搜索 (PPCAtt-NAS) 方法.
- 为了解决可微分架构中的计算消耗和深度差距问题,搜索.
主要方法:
- 实施了一种渐进式搜索策略,以逐步构建架构复杂性并执行分阶段的路径级修剪.
- 使用部分搜索方案,采用道级采样,以减少计算复杂性.
- 整合了注意力机制,以增强功能频道相关性和搜索能力.
主要成果:
- 拟议的PPCAtt-NAS方法有效地弥合了深度网络架构中的深度差距.
- 在架构搜索阶段实现了计算复杂性的显著降低.
- 与公共数据集上最先进的方法相比,证明了优越的架构性能.
结论:
- PPCAtt-NAS为神经架构搜索提供了一个高效和有效的解决方案.
- 该方法成功地减轻了DAS.中的计算负担和深度差距挑战.
- PPCAtt-NAS通过启用搜索高性能深度网络架构来推进该领域.
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