  Domain generalization (DG) is proposed to deal with the issue of domain
shift, which occurs when statistical differences exist between source and
target domains. However, most current methods do not account for a common
realistic scenario where the source and target domains have different classes.
To overcome this deficiency, open set domain generalization (OSDG) then emerges
as a more practical setting to recognize unseen classes in unseen domains. An
intuitive approach is to use multiple one-vs-all classifiers to define decision
boundaries for each class and reject the outliers as unknown. However, the
significant class imbalance between positive and negative samples often causes
the boundaries biased towards positive ones, resulting in misclassification for
known samples in the unseen target domain. In this paper, we propose a novel
meta-learning-based framework called dualistic MEta-learning with joint
DomaIn-Class matching (MEDIC), which considers gradient matching towards
inter-domain and inter-class splits simultaneously to find a generalizable
boundary balanced for all tasks. Experimental results demonstrate that MEDIC
not only outperforms previous methods in open set scenarios, but also maintains
competitive close set generalization ability at the same time. Our code is
available at https://github.com/zzwdx/MEDIC.
