An interesting research question we ask and answer positively in this paper is whether can we improve the performance of projection-based methods in UDA using self-training?
recent work on UDA (Morerio et al., 2018) has shown that minimising the entropy of a classifier on its predictions in the source and target domains is equivalent to learning a projection space that maximises the correlation between source and target instances.