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.
@article{arxiv.2008.07443,
title = {Zero Shot Domain Generalization},
author = {Udit Maniyar and Joseph K J and Aniket Anand Deshmukh and Urun Dogan and Vineeth N Balasubramanian},
journal= {arXiv preprint arXiv:2008.07443},
year = {2020}
}