  Unsupervised domain adaptation aims to transfer knowledge from a
fully-labeled source domain to an unlabeled target domain. However, in
real-world scenarios, providing abundant labeled data even in the source domain
can be infeasible due to the difficulty and high expense of annotation. To
address this issue, recent works consider the Few-shot Unsupervised Domain
Adaptation (FUDA) where only a few source samples are labeled, and conduct
knowledge transfer via self-supervised learning methods. Yet existing methods
generally overlook that the sparse label setting hinders learning reliable
source knowledge for transfer. Additionally, the learning difficulty difference
in target samples is different but ignored, leaving hard target samples poorly
classified. To tackle both deficiencies, in this paper, we propose a novel
Confidence-based Visual Dispersal Transfer learning method (C-VisDiT) for FUDA.
Specifically, C-VisDiT consists of a cross-domain visual dispersal strategy
that transfers only high-confidence source knowledge for model adaptation and
an intra-domain visual dispersal strategy that guides the learning of hard
target samples with easy ones. We conduct extensive experiments on Office-31,
Office-Home, VisDA-C, and DomainNet benchmark datasets and the results
demonstrate that the proposed C-VisDiT significantly outperforms
state-of-the-art FUDA methods. Our code is available at
https://github.com/Bostoncake/C-VisDiT.
