在SqueezeSEMAuto上为AutoML案例研究提供微架构设计探索模板
Chantana Chantrapornchai1, Supasit Kajkamhaeng2, Phattharaphon Romphet2
1Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand. fengcnc@ku.ac.th.
Scientific reports
|June 30, 2023
概括
本研究引入了一种AutoML框架,用于自动化卷积神经网络 (CNN) 架构搜索,显著提高图像识别任务的准确性,例如在不需要手动调整的情况下识别面部表情.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 模型对于图像识别至关重要,但需要广泛的手动调以获得最佳的架构.
- 自动机器学习 (AutoML) 提供了一种简化这一过程的解决方案.
研究的目的:
- 开发和评估一个AutoML框架,用于在CNN中探索微架构块和多输入选项.
- 通过使用SE块和剩余组合来增强SqueezeNet架构,以获得卓越的性能.
主要方法:
- 利用了一个AutoML框架,结合了随机,超带和贝叶斯搜索策略.
- 将框架应用于SqueezeNet,集成SE块和剩余组合.
- 在CIFAR-10和清华面部表情数据集上评估了性能.
主要成果:
- 实现了比传统架构更高的精度,无需人工努力.
- 在CIFAR-10上显示了显著的准确性增长 (在SE区块中高达78%;在传统SqueezeNet中高达50%).
- 显示了面部表情识别的实质性改进 (高达71%与传统模型的20%以下).
结论:
- 提出的AutoML方法有效地自动化了CNN架构优化,产生了卓越的准确性和可管理的模型大小.
- 这种方法显著减少了在深度学习模型设计中需要耗时的手动实验的需要.
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