  Unsupervised domain adaption aims to learn a powerful classifier for the
target domain given a labeled source data set and an unlabeled target data set.
To alleviate the effect of `domain shift', the major challenge in domain
adaptation, studies have attempted to align the distributions of the two
domains. Recent research has suggested that generative adversarial network
(GAN) has the capability of implicitly capturing data distribution. In this
paper, we thus propose a simple but effective model for unsupervised domain
adaption leveraging adversarial learning. The same encoder is shared between
the source and target domains which is expected to extract domain-invariant
representations with the help of an adversarial discriminator. With the labeled
source data, we introduce the center loss to increase the discriminative power
of feature learned. We further align the conditional distribution of the two
domains to enforce the discrimination of the features in the target domain.
Unlike previous studies where the source features are extracted with a fixed
pre-trained encoder, our method jointly learns feature representations of two
domains. Moreover, by sharing the encoder, the model does not need to know the
source of images during testing and hence is more widely applicable. We
evaluate the proposed method on several unsupervised domain adaption benchmarks
and achieve superior or comparable performance to state-of-the-art results.
