  Domain adaptation refers to the learning scenario that a model learned from
the source data is applied on the target data which have the same categories
but different distribution. While it has been widely applied, the distribution
discrepancy between source data and target data can substantially affect the
adaptation performance. The problem has been recently addressed by employing
adversarial learning and distinctive adaptation performance has been reported.
In this paper, a deep adversarial domain adaptation model based on a
multi-layer joint kernelized distance metric is proposed. By utilizing the
abstract features extracted from deep networks, the multi-layer joint
kernelized distance (MJKD) between the $j$th target data predicted as the $m$th
category and all the source data of the $m'$th category is computed. Base on
MJKD, a class-balanced selection strategy is utilized in each category to
select target data that are most likely to be classified correctly and treat
them as labeled data using their pseudo labels. Then an adversarial
architecture is used to draw the newly generated labeled training data and the
remaining target data close to each other. In this way, the target data itself
provide valuable information to enhance the domain adaptation. An analysis of
the proposed method is also given and the experimental results demonstrate that
the proposed method can achieve a better performance than a number of
state-of-the-art methods.
