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通过直角原型学习和错误拒绝校正的开放式长尾识别.

Binquan Deng1, Aouaidjia Kamel1, Chongsheng Zhang1

  • 1School of Computer and information Engineering, Henan University, 475004, Kaifeng, China.

Neural networks : the official journal of the International Neural Network Society
|October 18, 2024
PubMed
概括

本研究介绍了OLPR,这是一个开放式长尾识别的新框架. 通过学习直角原型和纠正错误拒绝,OLPR提高了分类准确性,超过了现有的方法.

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:

  • 从具有长尾和开放式分布的数据中学习在机器学习中提出了重大挑战.
  • 在这样的场景中,准确的分类对于现实世界的应用至关重要.

研究的目的:

  • 提出一个新的双流框架,OLPR (基于直角原型学习和错误拒绝校正的开放式长尾识别),以应对长尾和开放式数据分布的挑战.
  • 为了提高封闭集的分类准确性和开放集的识别性能.

主要方法:

  • OLPR采用双流架构,用于概率生成的概率预测学习 (PPL) 分支和用于学习直角类原型的距离度量学习 (DML) 分支.
  • DML分支使用直角原型损失,平衡的Softmin距离交叉损失和对抗性损失来实现紧的开放集表示.
  • 引入了一种代聚类模块 (ICM) 来分类开放式样本,并通过重新分类错误识别的已知样本来纠正错误拒绝.

主要成果:

  • 在ImageNet-LT,Places-LT,CIFAR-10/100-LT以及定制长尾开放式数据集上的实验证明了OLPR的有效性.
  • 与最先进的方法相比,OLPR在封闭式设置中实现了高达2.2%的整体分类精度改善.
  • 在开放式设置中,OLPR显示F-measure增加了4%.
关键词:
错误的拒绝错误的拒绝长尾的识别方式 长尾的识别方式开放式的长尾学习.原型学习学习的原型.

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结论:

  • 拟议的OLPR框架有效地处理图像分类中的长尾和开放式数据分布.
  • OLPR的双流方法与直角原型学习和错误拒绝校正在封闭式和开放式识别任务中提供了显著的性能提升.