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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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优化图像分类:自动化深度学习架构制作与网络和学习超参数调整调整.

Koon Meng Ang1, Wei Hong Lim1, Sew Sun Tiang1

  • 1Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia.

Biomimetics (Basel, Switzerland)
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ETLBOCBL-CNN,这是一种优化卷积神经网络 (CNN) 架构的自动化方法. 它在各种图像数据集上实现了高分类准确性,推进了智能设备基础设施.

关键词:
自动网络设计自动化网络设计深度学习架构的深度学习架构超参数优化超参数优化图像的分类图像的分类.教学基于学习的优化

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 卷积神经网络 (CNN) 对于图像分类至关重要.
  • 优化CNN架构是复杂和计算密集的.
  • 现有的方法经常与多样化和复杂的分类任务作斗争.

研究的目的:

  • 介绍ETLBOCBL-CNN,这是优化CNN架构的自动化方法.
  • 为了提高新和有效的CNN结构的发现.
  • 为了提高各种图像数据集的分类准确性.

主要方法:

  • ETLBOCBL-CNN使用了一个网络和学习超参数的编码方案.
  • 它结合了基于能力的学习和随机的同行互动.
  • 三个标准的选择方案优化了健身,多样性和改进率.

主要成果:

  • ETLBOCBL-CNN在9个图像数据集上实现了高精度,包括MNIST (99.72%) 和矩形 (99.99%).
  • 与最先进的技术相比,该方法显示出更高的性能.
  • 在不同复杂度的数据集中发现了显著的改进.

结论:

  • ETLBOCBL-CNN有效地自动化了CNN架构的优化.
  • 该方法显示了加强智能设备基础设施发展的巨大潜力.
  • 它的基于能力的学习和选择方案推动了CNN设计的创新.