EHM:通过高阶时刻指导的对比学习来探索无监督领域适应中的动态对齐和层次聚类
1School of Management, Zhejiang University, Hangzhou, 310058, China.
概括
本研究介绍了无监督域适应 (UDA) 的EHM框架. 通过使用动态调整和高阶对比学习,EHM提高了域调整和特征表示,提高了未标记数据上的模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 无监督域调整 (UDA) 旨在利用标记的源数据为未标记的目标数据.
- 最佳运输 (OT) 用于域差异,但由于正规化,它经常受到偏差估计的影响.
- 现有的方法可能会在对比学习和误导集群与条件正规化过程中失去歧视性信息.
研究的目的:
- 提出一个新的UDA框架,EHM,解决当前基于OT的UDA方法的局限性.
- 为了提高域差异估计的准确性和改善特征表示.
- 为了实现未标记的目标样本的强大聚类.
主要方法:
- 动态域对齐 (DDA) 使用动态调整的Sinkhorn分歧来减轻偏差.
- 可靠的高阶对比对齐 (RHCA) 为在高阶时刻空间中增强特征表示.
- 值得信赖的等级聚类 (THC) 集成多视图信息,以实现强大的聚类.
主要成果:
- EHM有效地消除了对域差异的偏见估计.
- RHCA显著改善了可转移的特征表示,并减少了类级域差异.
- THC有助于对未标记的目标样本进行强有力的聚类.
结论:
- 拟议的EHM框架在无监督域适应方面表现出显著的有效性.
- 在EHM中的新策略解决了现有UDA方法的关键挑战.
- 对各种基准的实验结果验证了EHM方法的优越性.
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