  Semi-supervised domain adaptation (SSDA) aims to bridge source and target
domain distributions, with a small number of target labels available, achieving
better classification performance than unsupervised domain adaptation (UDA).
However, existing SSDA work fails to make full use of label information from
both source and target domains for feature alignment across domains, resulting
in label mismatch in the label space during model testing. This paper presents
a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE),
to tackle this issue. Firstly, we introduce a cross-domain feature alignment
strategy, Inter-domain Mixup, that incorporates label information into model
adaptation. Specifically, we employ sample-level and manifold-level data mixing
to generate compatible training samples. These newly established samples,
combined with reliable and actual label information, display diversity and
compatibility across domains, while such extra supervision thus facilitates
cross-domain feature alignment and mitigates label mismatch. Additionally, we
utilize Neighborhood Expansion to leverage high-confidence pseudo-labeled
samples in the target domain, diversifying the label information of the target
domain and thereby further increasing the performance of the adaptation model.
Accordingly, the proposed approach outperforms existing state-of-the-art
methods, achieving significant accuracy improvements on popular SSDA
benchmarks, including DomainNet, Office-Home, and Office-31.
