  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
$\alpha$ while category $k+1\sim c$ are collected from domain $\beta$. 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.
