论文标题
混合设置域的适应
Mixed Set Domain Adaptation
论文作者
论文摘要
在常规域适应的设置中，源数据集的类别来自同一域（或多源域适应的域），这在现实中并不总是正确的。在本文中，我们建议\ textbf {\ textit {混合set域apation}（msda）}。在MSDA的设置下，源数据集的不同类别并非全部从同一域收集。例如，类别$ 1 \ sim k $是从域$α$收集的，而类别$ k+1 \ sim c $是从域$β$收集的。在这种情况下，由于源数据内部的分布差异，域的适应性性能将进一步影响。还提出了一种可以减少不同类别之间分布差异的特征元素加权方法（少数）方法。实验结果和质量分析显示了解决MSDA问题的重要性以及所提出方法的有效性。
In the settings of conventional domain adaptation, categories of the source dataset are from the same domain (or domains for multi-source domain adaptation), which is not always true in reality. In this paper, we propose \textbf{\textit{Mixed Set Domain Adaptation} (MSDA)}. Under the settings of MSDA, different categories of the source dataset are not all collected from the same domain(s). For instance, category $1\sim k$ are collected from domain $α$ while category $k+1\sim c$ are collected from domain $β$. Under such situation, domain adaptation performance will be further influenced because of the distribution discrepancy inside the source data. A feature element-wise weighting (FEW) method that can reduce distribution discrepancy between different categories is also proposed for MSDA. Experimental results and quality analysis show the significance of solving MSDA problem and the effectiveness of the proposed method.
