Our main intuition is to treat the problem as a semi-supervised learning task by considering target instances as unla-beled data, assuming the domain distance can be effectively reduced through domain-invariant representation learning.
the proposed approach jointly performs feature adaptation and semi-supervised learning in a multi-task learning setting.