  Standard supervised learning setting assumes that training data and test data
come from the same distribution (domain). Domain generalization (DG) methods
try to learn a model that when trained on data from multiple domains, would
generalize to a new unseen domain. We extend DG to an even more challenging
setting, where the label space of the unseen domain could also change. We
introduce this problem as Zero-Shot Domain Generalization (to the best of our
knowledge, the first such effort), where the model generalizes across new
domains and also across new classes in those domains. We propose a simple
strategy which effectively exploits semantic information of classes, to adapt
existing DG methods to meet the demands of Zero-Shot Domain Generalization. We
evaluate the proposed methods on CIFAR-10, CIFAR-100, F-MNIST and PACS
datasets, establishing a strong baseline to foster interest in this new
research direction.

Computer Vision and Pattern Recognition;Machine Learning