  Partial domain adaptation (PDA), in which we assume the target label space is
included in the source label space, is a general version of standard domain
adaptation. Since the target label space is unknown, the main challenge of PDA
is to reduce the learning impact of irrelevant source samples, named outliers,
which do not belong to the target label space. Although existing partial domain
adaptation methods effectively down-weigh outliers' importance, they do not
consider data structure of each domain and do not directly align the feature
distributions of the same class in the source and target domains, which may
lead to misalignment of category-level distributions. To overcome these
problems, we propose a graph partial domain adaptation (GPDA) network, which
exploits Graph Convolutional Networks for jointly considering data structure
and the feature distribution of each class. Specifically, we propose a label
relational graph to align the distributions of the same category in two domains
and introduce moving average centroid separation for learning networks from the
label relational graph. We demonstrate that considering data structure and the
distribution of each category is effective for PDA and our GPDA network
achieves state-of-the-art performance on the Digit and Office-31 datasets.
