  In semi-supervised domain adaptation, a few labeled samples per class in the
target domain guide features of the remaining target samples to aggregate
around them. However, the trained model cannot produce a highly discriminative
feature representation for the target domain because the training data is
dominated by labeled samples from the source domain. This could lead to
disconnection between the labeled and unlabeled target samples as well as
misalignment between unlabeled target samples and the source domain. In this
paper, we propose a novel approach called Cross-domain Adaptive Clustering to
address this problem. To achieve both inter-domain and intra-domain adaptation,
we first introduce an adversarial adaptive clustering loss to group features of
unlabeled target data into clusters and perform cluster-wise feature alignment
across the source and target domains. We further apply pseudo labeling to
unlabeled samples in the target domain and retain pseudo-labels with high
confidence. Pseudo labeling expands the number of ``labeled" samples in each
class in the target domain, and thus produces a more robust and powerful
cluster core for each class to facilitate adversarial learning. Extensive
experiments on benchmark datasets, including DomainNet, Office-Home and Office,
demonstrate that our proposed approach achieves the state-of-the-art
performance in semi-supervised domain adaptation.
