DRF-DRC:用于模型压缩的动态受体场和密集的残余连接
Wei Wang1, Yongde Zhang1, Liqiang Zhu2
1Avic Xi'an Aircraft Industry Group Company Ltd., Xi'an, 710089 China.
Cognitive neurodynamics
|November 17, 2023
概括
本研究介绍了DRENet,这是一种使用动态受感场操作和密集的剩余连接的新型神经架构搜索方法. 德伦网高效地设计计算机视觉网络,在多个基准上实现卓越的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度卷积神经网络 (CNN) 具有先进的计算机视觉,但手动架构设计是低效的.
- 神经架构搜索 (NAS) 是自动化网络设计的关键研究领域.
研究的目的:
- 提出一种新的NAS方法,DRENet,用于设计高效的深度神经网络.
- 将动态受体场 (DRF) 操作和可测量的密度剩余连接 (DRC) 引入NAS搜索空间.
主要方法:
- 开发了DRENet,将DRF和DRC纳入基于MobileNetV2的搜索空间.
- 在各种基准数据集上评估DRENet,包括CIFAR10/100,SVHN,CUB-200-2011,ImageNet和COCO.
- 将DRENet应用于铁路智能监控系统以进行实际验证.
主要成果:
- 在多个计算机视觉基准数据集中,DRENet表现出卓越的性能.
- 拟议的DRF运行和DRC将提高网络的效率和有效性.
- 在现实世界铁路智能监控系统中成功应用.
结论:
- 德伦网为计算机视觉的神经架构搜索提供了一种有效和高效的方法.
- 集成DRF和DRC有助于改进网络设计和性能.
- 该方法对智能监控及其他领域的实际应用具有前景.
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