  Unsupervised domain adaptation seeks to mitigate the distribution discrepancy
between source and target domains, given labeled samples of the source domain
and unlabeled samples of the target domain. Generative adversarial networks
(GANs) have demonstrated significant improvement in domain adaptation by
producing images which are domain specific for training. However, most of the
existing GAN based techniques for unsupervised domain adaptation do not
consider semantic information during domain matching, hence these methods
degrade the performance when the source and target domain data are semantically
different. In this paper, we propose an end-to-end novel semantic consistent
generative adversarial network (SCGAN). This network can achieve source to
target domain matching by capturing semantic information at the feature level
and producing images for unsupervised domain adaptation from both the source
and the target domains. We demonstrate the robustness of our proposed method
which exceeds the state-of-the-art performance in unsupervised domain
adaptation settings by performing experiments on digit and object
classification tasks.
