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FGPLFA:精细的伪标签和特征对齐,用于源代码免费的无监督域名适应

Zhongyi Wen, Qiang Li, Yatong Wang

IEEE transactions on neural networks and learning systems

|October 7, 2025

概括

这项研究引入了细粒度的伪标签和特征对齐 (FGPLFA),以改善无源无监管域调整. FGPLFA通过减少杂的伪标签和调整功能以提高目标域的适应性来提高模型性能.

科学领域:

机器学习机器学习
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

无源无监管域调整 (SFUDA) 对于保护隐私的机器学习至关重要.
当前的SFUDA方法经常使用伪标签,这可能会造成噪音并降低性能.
现有的技术忽略了细粒度数据特征,限制了适应的有效性.

研究的目的:

开发一种新的方法,FGPLFA,以提高SFUDA的性能.
解决现有的SFUDA方法中噪音伪标签的局限性.
在没有源数据访问的情况下,提高目标领域的模型适应性.

主要方法:

引入基于梯度的指标,整合模型知识和数据特征,以进行可靠的样本评估.
开发了细粒度伪标签 (FGPL) 模块,用于样本级数据集群和类别/领域特定的伪标签.
实现的平均共变性调整特征对齐 (MCAFA) 对于跨子集的顺序特征对齐.

主要成果:

通过多层次的细粒度,FGPLFA显著减少了噪音的伪标签.
该方法增强了特征对齐,提高了模型的适应性.
跨多个数据集的实验验证证证了FGPLFA的卓越性能.

结论:

对于SFUDA,FGPLFA提供了一个强大的解决方案,其性能优于现有的方法.
拟议的FGPL和MCAFA模块有效地解决了域调整的关键挑战.
这项工作促进了SFUDA的发展,通过在数据限制下实现更准确和更适应的模型.

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