  Adversarial adaptation models have demonstrated significant progress towards
transferring knowledge from a labeled source dataset to an unlabeled target
dataset. Partial domain adaptation (PDA) investigates the scenarios in which
the source domain is large and diverse, and the target label space is a subset
of the source label space. The main purpose of PDA is to identify the shared
classes between the domains and promote learning transferable knowledge from
these classes. In this paper, we propose a multi-class adversarial architecture
for PDA. The proposed approach jointly aligns the marginal and
class-conditional distributions in the shared label space by minimaxing a novel
multi-class adversarial loss function. Furthermore, we incorporate effective
regularization terms to encourage selecting the most relevant subset of source
domain classes. In the absence of target labels, the proposed approach is able
to effectively learn domain-invariant feature representations, which in turn
can enhance the classification performance in the target domain. Comprehensive
experiments on three benchmark datasets Office-31, Office-Home, and
Caltech-Office corroborate the effectiveness of the proposed approach in
addressing different partial transfer learning tasks.
