  With the goal of directly generalizing trained model to unseen target
domains, domain generalization (DG), a newly proposed learning paradigm, has
attracted considerable attention. Previous DG models usually require a
sufficient quantity of annotated samples from observed source domains during
training. In this paper, we relax this requirement about full annotation and
investigate semi-supervised domain generalization (SSDG) where only one source
domain is fully annotated along with the other domains totally unlabeled in the
training process. With the challenges of tackling the domain gap between
observed source domains and predicting unseen target domains, we propose a
novel deep framework via joint domain-aware labels and dual-classifier to
produce high-quality pseudo-labels. Concretely, to predict accurate
pseudo-labels under domain shift, a domain-aware pseudo-labeling module is
developed. Also, considering inconsistent goals between generalization and
pseudo-labeling: former prevents overfitting on all source domains while latter
might overfit the unlabeled source domains for high accuracy, we employ a
dual-classifier to independently perform pseudo-labeling and domain
generalization in the training process. When accurate pseudo-labels are
generated for unlabeled source domains, the domain mixup operation is applied
to augment new domains between labeled and unlabeled domains, which is
beneficial for boosting the generalization capability of the model. Extensive
results on publicly available DG benchmark datasets show the efficacy of our
proposed SSDG method.
