  In semi-supervised domain adaptation (SSDA), a few labeled target samples of
each class help the model to transfer knowledge representation from the fully
labeled source domain to the target domain. Many existing methods ignore the
benefits of making full use of the labeled target samples from multi-level. To
make better use of this additional data, we propose a novel Prototype-based
Multi-level Learning (ProML) framework to better tap the potential of labeled
target samples. To achieve intra-domain adaptation, we first introduce a
pseudo-label aggregation based on the intra-domain optimal transport to help
the model align the feature distribution of unlabeled target samples and the
prototype. At the inter-domain level, we propose a cross-domain alignment loss
to help the model use the target prototype for cross-domain knowledge transfer.
We further propose a dual consistency based on prototype similarity and linear
classifier to promote discriminative learning of compact target feature
representation at the batch level. Extensive experiments on three datasets,
including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed
method achieves state-of-the-art performance in SSDA.
