  The task of unsupervised domain adaptation is proposed to transfer the
knowledge of a label-rich domain (source domain) to a label-scarce domain
(target domain). Matching feature distributions between different domains is a
widely applied method for the aforementioned task. However, the method does not
perform well when classes in the two domains are not identical. Specifically,
when the classes of the target correspond to a subset of those of the source,
target samples can be incorrectly aligned with the classes that exist only in
the source. This problem setting is termed as partial domain adaptation (PDA).
In this study, we propose a novel method called Two Weighted
Inconsistency-reduced Networks (TWINs) for PDA. We utilize two classification
networks to estimate the ratio of the target samples in each class with which a
classification loss is weighted to adapt the classes present in the target
domain. Furthermore, to extract discriminative features for the target, we
propose to minimize the divergence between domains measured by the classifiers'
inconsistency on target samples. We empirically demonstrate that reducing the
inconsistency between two networks is effective for PDA and that our method
outperforms other existing methods with a large margin in several datasets.
