通过相互信息最大化来保护域内私人信息
Jiahong Chen1, Jing Wang1, Weipeng Lin2
1Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
概括
这项研究引入了一种新的无监督域适应方法,该方法保留了独特的数据特征. 通过最大化相互信息,它增强了跨不同数据集的模型概括性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 无监督域调整 (UDA) 旨在改善对新数据域的模型概括.
- 当前的UDA方法专注于域不变特征,但往往会丢弃有价值的域特定信息.
- 这些域特定信息对于强大的跨域概括至关重要.
研究的目的:
- 提出一种新的UDA方法,可以保留域私密信息,同时确保域不变的特征.
- 通过保留未标记的目标域的独特统计数据来增强跨域概括.
- 为了验证保护UDA领域特定信息的有效性.
主要方法:
- 利用相互信息来保护潜在特征中的域特定信息.
- 同时最大限度地增加相互信息,并最大限度地减少域差异.
- 使用神经估计器来量化输入和潜伏空间之间的相互信息.
主要成果:
- 拟议的方法有效地保留了域私有信息,从而改善了概括性.
- 同时优化相互信息和域差异被证明是有效的.
- 理论分析和经验结果证实了保护独特领域信息的重要性.
结论:
- 保护域私有信息对于优越的跨域概括在UDA中至关重要.
- 这种新方法在基准数据集上表现优于现有的最先进技术.
- 这项工作为开发更有效的UDA模型提供了新的方向.
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