论文标题
零射击域的概括
Zero Shot Domain Generalization
论文作者
论文摘要
标准监督学习设置假定培训数据和测试数据来自相同的分布（域）。域的概括（DG）方法试图学习一个模型，该模型在从多个域进行培训时，将概括为一个新的看不见的域。我们将DG扩展到更具挑战性的环境，在这里，看不见的域的标签空间也可能会改变。我们将这个问题介绍为零击领域的概括（据我们所知，这是第一个这样的工作），该模型跨新领域以及这些域中的新阶层都概括了。我们提出了一个简单的策略，该策略有效利用了类的语义信息，以适应现有的DG方法以满足零击域域概括的需求。我们评估了CIFAR-10，CIFAR-100，F-MNIST和PACS数据集的提议方法，建立了强大的基准，以促进这一新的研究方向的兴趣。
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.
