Our goal is to develop a self-training algorithm that can substantially enhance DNN models in cases where labeled training data are scarce.
we are focusing ( § 5) on the lightly supervised setup where only a small in-domain labeled dataset is available, and on the domain adaptation setup where the labeled dataset may be large but it comes from a different domain than the one to which the model is meant to be applied.