  The goal of domain generalization is to learn from multiple source domains to
generalize to unseen target domains under distribution discrepancy. Current
state-of-the-art methods in this area are fully supervised, but for many
real-world problems it is hardly possible to obtain enough labeled samples. In
this paper, we propose the first method of domain generalization to leverage
unlabeled samples, combining of meta learning's episodic training and
semi-supervised learning, called DGSML. DGSML employs an entropy-based
pseudo-labeling approach to assign labels to unlabeled samples and then
utilizes a novel discrepancy loss to ensure that class centroids before and
after labeling unlabeled samples are close to each other. To learn a
domain-invariant representation, it also utilizes a novel alignment loss to
ensure that the distance between pairs of class centroids, computed after
adding the unlabeled samples, is preserved across different domains. DGSML is
trained by a meta learning approach to mimic the distribution shift between the
input source domains and unseen target domains. Experimental results on
benchmark datasets indicate that DGSML outperforms state-of-the-art domain
generalization and semi-supervised learning methods.
