预测通用领域适应的共同标签的预测
Xinxin Shan1, Tai Ma1, Ying Wen1
1Shanghai Key Laboratory of Multidimensional Information Processing, School of Communications and Electronic Engineering, East China Normal University, Shanghai, 200241, China.
概括
本研究引入了一种全局域适应 (UniDA) 的新方法,该方法预测了共同的标签,改善了具有不同标签集的数据集之间的知识转移,并减少了负面转移.
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:
- 全面域名适应 (UniDA) 解决了具有不同标签集的域名之间的知识转移.
- 现有的UniDA方法难以预测通用标签和手动设置值,导致负面转移问题.
研究的目的:
- 为UniDA提出一种新的分类模型,即通用标签预测 (PCL).
- 解决预测共同标签和减轻负面转移现有方法的局限性.
主要方法:
- 为UniDA.com开发了通用标签预测 (PCL) 模型.
- 通过集群 (CSC) 使用分类分离来预测共同的标签.
- 引入了一个新的评估指标,类别分离精度.
- 根据预测的共同标签选择源样本,以微调模型并减少负面转移.
主要成果:
- 拟议的PCL模型有效地预测了UniDA中的共同标签.
- 类别分离的准确性被证明是评估类别分离性能的可靠指标.
- 该方法在减少负转移和改善域对齐方面表现出有效性.
- 在基准数据集上的实验结果验证了拟议方法的有效性.
结论:
- PCL模型在通用域调整中提供了显著的进步.
- 预测通用标签和使用分类对有效的UniDA至关重要.
- 拟议的方法增强了域调整,并最大限度地减少了负转移,从而提高了性能.
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