  Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from
a fully labeled source domain to a scarcely labeled target domain. In this
paper, we propose a Multi-level Consistency Learning (MCL) framework for SSDA.
Specifically, our MCL regularizes the consistency of different views of target
domain samples at three levels: (i) at inter-domain level, we robustly and
accurately align the source and target domains using a prototype-based optimal
transport method that utilizes the pros and cons of different views of target
samples; (ii) at intra-domain level, we facilitate the learning of both
discriminative and compact target feature representations by proposing a novel
class-wise contrastive clustering loss; (iii) at sample level, we follow
standard practice and improve the prediction accuracy by conducting a
consistency-based self-training. Empirically, we verified the effectiveness of
our MCL framework on three popular SSDA benchmarks, i.e., VisDA2017, DomainNet,
and Office-Home datasets, and the experimental results demonstrate that our MCL
framework achieves the state-of-the-art performance.
