Abstract
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However, such an assumption is no longer valid in a more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.