  This paper proposes a novel approach for unsupervised domain adaptation (UDA)
with target shift. Target shift is a problem of mismatch in label distribution
between source and target domains. Typically it appears as class-imbalance in
target domain. In practice, this is an important problem in UDA; as we do not
know labels in target domain datasets, we do not know whether or not its
distribution is identical to that in the source domain dataset. Many
traditional approaches achieve UDA with distribution matching by minimizing
mean maximum discrepancy or adversarial training; however these approaches
implicitly assume a coincidence in the distributions and do not work under
situations with target shift. Some recent UDA approaches focus on class
boundary and some of them are robust to target shift, but they are only
applicable to classification and not to regression.
  To overcome the target shift problem in UDA, the proposed method, partially
shared variational autoencoders (PS-VAEs), uses pair-wise feature alignment
instead of feature distribution matching. PS-VAEs inter-convert domain of each
sample by a CycleGAN-based architecture while preserving its label-related
content. To evaluate the performance of PS-VAEs, we carried out two
experiments: UDA with class-unbalanced digits datasets (classification), and
UDA from synthesized data to real observation in human-pose-estimation
(regression). The proposed method presented its robustness against the
class-imbalance in the classification task, and outperformed the other methods
in the regression task with a large margin.
