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.

本文提出了一种称为 DGSML 的方法，它结合了元学习的情节训练和半监督学习，利用基于熵的伪标签方法为无标签样本分配标签，并利用新的差异性损失和对齐损失实现域不变表示的学习，进而提高领域泛化能力。实验结果表明，DGSML 优于现有领域泛化和半监督方法。