  In this paper, we study the problem of unsupervised domain adaptation that
aims at obtaining a prediction model for the target domain using labeled data
from the source domain and unlabeled data from the target domain. There exists
an array of recent research based on the idea of extracting features that are
not only invariant for both domains but also provide high discriminative power
for the target domain. In this paper, we propose an idea of empowering the
discriminativeness: Adding a new, artificial class and training the model on
the data together with the GAN-generated samples of the new class. The trained
model based on the new class samples is capable of extracting the features that
are more discriminative by repositioning data of current classes in the target
domain and therefore drawing the decision boundaries more effectively. Our idea
is highly generic so that it is compatible with many existing methods such as
DANN, VADA, and DIRT-T. We conduct various experiments for the standard data
commonly used for the evaluation of unsupervised domain adaptations and
demonstrate that our algorithm achieves the SOTA performance for many
scenarios.
