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动态双向数据重组,以实现半监督学习中的有效道路垃圾细分.

Suheng Peng1, Jiacai Liao2, Libo Cao1

  • 1School of Mechanical and Vehicle Engineering, Hunan University, Lushan South Road, Yuelu District, Changsha, Hunan Province, Changsha, 410082, Hunan, China.

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概括

本研究介绍了动态双向数据复制 (DBDR) 以提高使用半监督学习 (SSL) 的道路垃圾细分. DBDR通过动态平衡标记和未标记数据来提高模型性能,克服城市废物管理中的注释限制.

关键词:
深度学习是一种深度学习.垃圾分类垃圾分类语义细分 语义细分是指语义细分.半监督学习 半监督学习

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科学领域:

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

背景情况:

  • 深度神经网络 (DNN) 对于道路垃圾细分是有效的,但需要昂贵的像素级注释.
  • 在有效的城市废物管理中,平衡注释成本和细分精度至关重要.
  • 半监督学习 (SSL) 利用未标记的数据来减少注释依赖,但在极端注释不平衡下,它与数据多样性作斗争.

研究的目的:

  • 为解决SSL中的代表性停滞问题,以解决因稀缺,非多样化的标签数据引起的道路垃圾细分问题.
  • 引入一种新的机制,即动态双向数据推 (DBDR),通过优化数据交互来提高SSL性能.
  • 提高智能城市废物管理技术的经济可行性.

主要方法:

  • 开发了用于SSL的动态双向数据重组 (DBDR) 机制.
  • 将标记数据集成到未标记的流中,基于早期培训期间的信心水平.
  • 在中期培训期间利用动态内存队列和双重验证来从未标记的数据转移到标记的数据.
  • 确保DBDR与现有的主流SSL框架的兼容性.

主要成果:

  • 与五个最先进的基线模型相比,DBDR在真实世界的道路垃圾数据集上显著提高了性能.
  • 废弃实验证实了DBDR在对塑料和纸张等具有挑战性,视觉相似的目标进行细分方面的有效性.
  • 该方法证明了数据信息的改进利用,并克服了模型性能停滞.

结论:

  • 在极端注释不平衡的情况下,DBDR有效地解决了SSL中的表示停滞.
  • 拟议的机制为道路垃圾细分提供了显著的性能改进.
  • DBDR为推进智慧城市废物管理基础设施提供了一个经济可行的解决方案.