Armed with the pseudo-labelled data generated via Algorithm 1, we can now learn a projection for the target domain, T prj , following the same procedure we proposed for learning S prj in Section 3.1.
as stated above, our proposed method Self-adapt, differs from the prior work discussed above in that it (a) does not require pivots, (b) does not require multiple feature views, (c) learns two different projections for the source and target domains and (d) combines a projection and a selftraining step in a non-iterative manner to improve the performance in UDa.