  This paper proposes an importance weighted adversarial nets-based method for
unsupervised domain adaptation, specific for partial domain adaptation where
the target domain has less number of classes compared to the source domain.
Previous domain adaptation methods generally assume the identical label spaces,
such that reducing the distribution divergence leads to feasible knowledge
transfer. However, such an assumption is no longer valid in a more realistic
scenario that requires adaptation from a larger and more diverse source domain
to a smaller target domain with less number of classes. This paper extends the
adversarial nets-based domain adaptation and proposes a novel adversarial
nets-based partial domain adaptation method to identify the source samples that
are potentially from the outlier classes and, at the same time, reduce the
shift of shared classes between domains.
