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进化神经架构搜索,结合多分支ConvNet和改进的变压器
1College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, China.
Scientific reports
|September 22, 2023
概括
本研究介绍了一种使用变压器和ConvNets自动设计高效深度学习模型的进化神经架构搜索 (ENAS) 方法. 这种方法提高了性能,并降低了复杂网络架构的计算成本.
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:
- 深层卷积神经网络 (CNN) 的手动设计是具有挑战性的,因为它具有复杂的拓.
- 现有的神经架构搜索 (NAS) 方法经常面临高计算成本或设计不灵活性.
研究的目的:
- 为设计CNN架构提出一种高效灵活的自动化方法.
- 为了解决当前NAS技术的局限性.
主要方法:
- 开发了一个进化神经架构搜索 (ENAS) 框架,将改进的变压器与多分支卷积神经网络 (ConvNet) 集成.
- 引入了一种新的"无批量规范化变压器块" (BFNTBlock),结合无批量规范化 (BFN) 和批量规范化 (BN),以改善特征表示和减轻估计转移.
- 多分支区块增强了功能空间丰富性和网络代表能力.
主要成果:
- 拟议的ENAS方法在CIFAR10上实现了97.24%的高精度,在CIFAR100上达到80.06%.
- 该方法表现出高计算效率,只需要1.46和1.53个GPU天进行CIFAR10和CIFAR100的训练.
- 与常见方法相比,在真实世界数据集 (GTSRB和NEU-CLS) 上观察到更高的性能,验证了其普遍性.
结论:
- 提出的ENAS方法有效地自动化了高性能和计算效率高的深度神经网络的设计.
- BFNTBlock设计证明有利于利用本地和远程依赖,同时提高模型稳定性.
- 该方法在各种计算机视觉任务中显示出强大的概括能力.
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