  Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with
many labeled source data from a related domain. Current SSDA approaches usually
aim at aligning the target data to the labeled source data with feature space
mapping and pseudo-label assignments. Nevertheless, such a source-oriented
model can sometimes align the target data to source data of the wrong classes,
degrading the classification performance. This paper presents a novel
source-adaptive paradigm that adapts the source data to match the target data.
Our key idea is to view the source data as a noisily-labeled version of the
ideal target data. Then, we propose an SSDA model that cleans up the label
noise dynamically with the help of a robust cleaner component designed from the
target perspective. Since the paradigm is very different from the core ideas
behind existing SSDA approaches, our proposed model can be easily coupled with
them to improve their performance. Empirical results on two state-of-the-art
SSDA approaches demonstrate that the proposed model effectively cleans up the
noise within the source labels and exhibits superior performance over those
approaches across benchmark datasets. Our code is available at
https://github.com/chu0802/SLA .

Computer Vision and Pattern Recognition