Domain adaptation (DA) solves this problem by building a model utilizing the knowledge of label-rich dataset, i.e., source domain, which generalizes well on the label-scarce dataset, i.e., target domain.
it means that inference of pseudo labels is easily influenced by imbalance distribution of samples in different classes where certain classes would dominate the process of self-training and cause errors accumulation.