ProxyMix:以代理为基础的混合培训与标签精炼厂,用于无源域调整域名
Yuhe Ding1, Lijun Sheng2, Jian Liang3
1School of Computer Science and Technology, Anhui University, China.
概括
无源无监管域调整 (SFDA) 方法现在可以避免额外的参数和杂的标签. ProxyMix使用了分类器原型和一种新的标签精炼战略,以改善域调整任务中的知识传输.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 无源无监管域调整 (SFDA) 对于没有原始源数据的域之间转移知识至关重要,解决隐私和传输问题.
- 现有的SFDA方法通常需要额外的参数或依赖于杂的伪标签,限制其有效性.
- 在各种应用中,对高效准确的SFDA技术的需求在不断增长.
研究的目的:
- 提出ProxyMix,一种有效的SFDA方法,可以避免额外的参数和杂的伪标签.
- 为了利用预先训练的源代码模型,并加强知识转移到未标记的目标领域.
- 提高伪标签的可靠性,减轻噪音数据的负面影响.
主要方法:
- ProxyMix将分类器权重定义为类原型,并使用最近的邻居构建一个平衡的代理源域.
- 频率加权聚合策略为未标记的目标数据生成可靠的软伪标签.
- 域间和域内混杂调整调整代理和目标域,利用目标特征的内部结构.
主要成果:
- ProxyMix在2D图像和3D点云对象识别基准上实现了最先进的性能.
- 拟议的方法有效地减轻了噪音标签的负面影响,通过标签精炼厂.
- 在无源域调整场景中,ProxyMix展示了卓越的知识传输能力.
结论:
- ProxyMix为无源无监管域名适应提供了一个有效的解决方案,其性能优于现有的方法.
- 这种新方法解决了以前的SFDA技术的关键局限性,提高了模型的稳定性和准确性.
- 对于需要保护隐私的域名调整的真实世界应用程序,ProxyMix显示出显著的前景.
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