  Domain adaptation is a potential method to train a powerful deep neural
network, which can handle the absence of labeled data. More precisely, domain
adaptation solving the limitation called dataset bias or domain shift when the
training dataset and testing dataset are extremely different. Adversarial
adaptation method becoming popular among other domain adaptation methods.
Relies on the idea of GAN, adversarial domain adaptation tries to minimize the
distribution between training and testing datasets base on the adversarial
object. However, some conventional adversarial domain adaptation methods cannot
handle large domain shifts between two datasets or the generalization ability
of these methods are inefficient. In this paper, we propose an improved
adversarial domain adaptation method called Semi-Supervised Adversarial
Discriminative Domain Adaptation (SADDA), which can overcome the limitation of
other domain adaptation. We also show that SADDA has better performance than
other adversarial adaptation methods and illustrate the promise of our method
on digit classification and emotion recognition problems.
