  Semi-supervised domain adaptation (SSDA) methods have demonstrated great
potential in large-scale image classification tasks when massive labeled data
are available in the source domain but very few labeled samples are provided in
the target domain. Existing solutions usually focus on feature alignment
between the two domains while paying little attention to the discrimination
capability of learned representations in the target domain. In this paper, we
present a novel and effective method, namely Effective Label Propagation (ELP),
to tackle this problem by using effective inter-domain and intra-domain
semantic information propagation. For inter-domain propagation, we propose a
new cycle discrepancy loss to encourage consistency of semantic information
between the two domains. For intra-domain propagation, we propose an effective
self-training strategy to mitigate the noises in pseudo-labeled target domain
data and improve the feature discriminability in the target domain. As a
general method, our ELP can be easily applied to various domain adaptation
approaches and can facilitate their feature discrimination in the target
domain. Experiments on Office-Home and DomainNet benchmarks show ELP
consistently improves the classification accuracy of mainstream SSDA methods by
2%~3%. Additionally, ELP also improves the performance of UDA methods as well
(81.5% vs 86.1%), based on UDA experiments on the VisDA-2017 benchmark. Our
source code and pre-trained models will be released soon.
