增量信心采样与域调整的最佳传输
Mourad El Hamri1, Younès Bennani2, Issam Falih3
1BioSTM, UR 7537, Université Paris Cité, Paris, France.
International journal of neural systems
|June 12, 2024
概括
这项研究介绍了OTP-DA,一种新的无监督域适应方法. 它使用最优的传输伪标签,使有效的域不变学习没有目标标签.
科学领域:
- 机器学习 机器学习
- 统计学学习理论
背景情况:
- 域调整处理源域和目标域之间的数据分布转移.
- 无监督域名适应缺乏目标域名中的标记数据,这构成了重大挑战.
研究的目的:
- 介绍OTP-DA,一种用于无监督域调整的增量方法.
- 开发一种学习域不变且分离良好的联合子空间的方法.
主要方法:
- 使用线性差异分析 (LDA) 来学习联合子空间.
- 采用基于最佳传输 (OTP) 的选择性标签传播技术,为目标数据生成伪标签.
- 实现了一个自我训练机制,在潜伏子空间内通过伪标签来促进.
主要成果:
- 在视觉域适应任务中,OTP-DA表现出有希望的有效性和稳定性.
- 与最先进的方法相比,拟议的方法表现良好.
- 理论分析为有效的无监督域调整提供了条件.
结论:
- 在不受监督的域名调整中,OTP-DA有效地克服了目标域名标签的缺乏.
- 优化运输和自我培训的整合为领域转移问题提供了强大的解决方案.
- 该方法通过对视觉领域适应基准进行广泛的实验来验证.
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