普遍且可扩展的弱监督域名适应
概括
本研究介绍了PDCAS,这是一种新的弱监督域适应方法,可以处理噪音数据而不需要知道噪音率. 它还扩展到MSPDCAS的多源场景,改善域调整性能.
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:
- 域名适应旨在将知识从标记的源域转移到未标记的目标域.
- 现实世界的数据往往含有噪音,需要弱监督域适应 (WSDA) 方法.
- 现有的WSDA方法需要事先了解噪声率,这限制了实际应用.
研究的目的:
- 开发一种通用且可扩展的WSDA方法,不需要事先了解噪声率.
- 在多源场景中解决单源领域适应的局限性.
- 在有噪音数据的情况下,提高域调整的稳定性和有效性.
主要方法:
- 建议PDCAS有两个阶段:渐进蒸和域调整.
- 渐进蒸反复提炼无监督的噪音源样本.
- 域调整使用类调整采样来平衡源域和目标域样本以及全球特征分布.
主要成果:
- 在源域中,PDCAS有效处理标签和特征噪音.
- 拟议的MSPDCAS证明了多源噪音域适应的可扩展性.
- 在Office-31和Office-Home数据集上的实验表明,与最先进的方法相比,性能优越.
结论:
- 对于弱监督的域调整,PDCAS提供了一个强大的和可通用的解决方案.
- 该框架的可扩展性通过其扩展到多源设置来验证.
- 该方法在杂的现实环境中显著提高了域调整准确性和稳定性.
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