  In this work, we present a method for unsupervised domain adaptation. Many
adversarial learning methods train domain classifier networks to distinguish
the features as either a source or target and train a feature generator network
to mimic the discriminator. Two problems exist with these methods. First, the
domain classifier only tries to distinguish the features as a source or target
and thus does not consider task-specific decision boundaries between classes.
Therefore, a trained generator can generate ambiguous features near class
boundaries. Second, these methods aim to completely match the feature
distributions between different domains, which is difficult because of each
domain's characteristics.
  To solve these problems, we introduce a new approach that attempts to align
distributions of source and target by utilizing the task-specific decision
boundaries. We propose to maximize the discrepancy between two classifiers'
outputs to detect target samples that are far from the support of the source. A
feature generator learns to generate target features near the support to
minimize the discrepancy. Our method outperforms other methods on several
datasets of image classification and semantic segmentation. The codes are
available at \url{https://github.com/mil-tokyo/MCD_DA}
