  In computer vision, unsupervised domain adaptation (UDA) is an approach to
transferring knowledge from a label-rich source domain to a fully-unlabeled
target domain. Conventional UDA approaches have two problems. The first problem
is that a class classifier can be biased to the source domain because it is
trained using only source samples. The second is that previous approaches align
image-level features regardless of foreground and background, although the
classifier requires foreground features. To solve these problems, we introduce
Weight-based Mask Network (WEMNet) composed of Domain Ignore Module (DIM) and
Semantic Enhancement Module (SEM). DIM obtains domain-agnostic feature
representations via the weight of the domain discriminator and predicts
categories. In addition, SEM obtains class-related feature representations
using the classifier weight and focuses on the foreground features for domain
adaptation. Extensive experimental results reveal that the proposed WEMNet
outperforms the competitive accuracy on representative UDA datasets.
