基于值的黑盒噪音标签的利用不受监督的域名适应
Huiwen Xu1, Jaeri Lee1, U Kang1
1Data Mining Lab, Seoul National University, Seoul, Republic of Korea.
PloS one
|May 12, 2025
概括
本研究介绍了基于值的噪音预测利用 (TEN) 对于黑子无监督域调整. 网络精确地将知识从源模型传输到目标域,即使有噪音标签,也能提高模型性能.
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:
- 无监督域调整 (UDA) 旨在将知识从一个标记的源域转移到一个没有标记的目标域.
- 黑盒 UDA 专门针对源数据或模型参数无法访问的场景,通常是由于隐私问题.
- 现有的方法在源模型产生的噪音标签上扎,这可能会降低目标模型的性能.
研究的目的:
- 开发一个强大的黑子无监督域名适应方法,有效地处理噪音标签.
- 为了提高知识从预先训练的黑子源模型转移到未标记的目标域的准确性和可靠性.
主要方法:
- 拟议的基于值的噪声预测利用 (TEN) 方法.
- 采用了基于值的方法来区分清洁和噪音标签与黑盒源模型.
- 利用特定类的灵活值来管理难以学习的类.
- 集成的知识蒸用于清洁数据和负面学习用于噪音标签.
主要成果:
- 与现有的基线方法相比,TEN表现优越.
- 在黑盒无监督域名调整任务中,在黑盒无监督域名调整任务中,获得了高达9.49%的显著精度改进.
- 从源模型中有效地保留了高可靠性知识,尽管存在域间隙和标签噪声.
结论:
- 通过智能处理杂标签,TEN为黑子无监督域名适应提供了有效的解决方案.
- 拟议的方法通过区分和利用高可信度预测来增强知识传输.
- 在机器学习中,TEN为保护隐私的模型适应提供了一个有希望的方向.
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