无监督域名适应的逐步决策边界移动
IEEE transactions on neural networks and learning systems
|August 9, 2024
概括
本研究引入了一种渐进的决策边界转移算法,以改善无监督域适应 (UDA). 该方法解决了目标数据中的语义不确定性,增强了模型概括性,并优于当前最先进的方法.
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:
- 无监督域名适应 (UDA) 解决了标记源域和未标记目标域之间的域名转移.
- 目前使用双分类器和自我训练的UDA方法与从语义上不确定的实例中积累的错误作斗争.
研究的目的:
- 开发一种用于无监督域调整的新算法,以减轻语义不确定的目标实例中的错误积累.
- 通过增强实例可区分性和类别级别对齐,提高目标领域的特定任务概括性.
主要方法:
- 设计了一个渐进的决策边界转移算法来建模和利用语义不确定性.
- 引入不确定性解,使用对比学习从低不确定性实例中提取歧视性信息.
- 在高不确定性实例中最小化预测,以减少预测信心.
主要成果:
- 拟议的方法有效地模拟了目标域数据中的语义不确定性.
- 与现有的UDA技术相比,实现了更好的可区分性和类别级别对齐.
- 在三个基准数据集上,与最先进的UDA方法相比,表现优越.
结论:
- 渐进的决策边界转移算法为无监督域调整提供了强大的解决方案.
- 解决语义不确定性对于防止UDA中的可歧视性退化和类别错位至关重要.
- 拟议的方法显著提升了UDA模型在现实世界应用中的功能.
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