Zero-shot learning (ZSL) aims to recognize objects of target classes by transferring knowledge from source classes through the semantic embeddings bridging. However, ZSL focuses the recognition only on unseen classes, which is unreasonable in realistic scenarios. A more reasonable way is to recognize new samples on combined domains, namely Generalized Zero Shot Learning (GZSL). Due to the fact that the source domain and target domain are disjoint and have unrelated classes potentially, ZSL and GZSL often suffer from the problem of projection domain shift. Besides, some semantic embeddings of prototypes are very similar, which makes the recognition less discriminative. To circumvent these issues, in this paper, we propose a novel method, called Learning Discriminative Domain-Invariant Prototypes (DDIP). In DDIP, both target and source domains are combined and projected into a hyper-spherical space, which is automatically learned by a regularized dictionary learning. In addition, an orthogonal constraint is employed to the latent hyper-spherical space to ensure all the class prototypes, including seen classes and unseen classes, to be orthogonal to each other to make them more discriminative. Extensive experiments on four popular benchmark and a large-scale datasets are conducted on both GZSL and standard ZSL settings, and the results show that our DDIP can outperform the state-of-the-art methods.

零镜头学习（ZSL）旨在通过语义嵌入桥接从源类传递知识来识别目标类的对象。但是，ZSL仅将识别重点放在看不见的类上，这在现实情况下是不合理的。一种更合理的方法是识别组合域上的新样本，即广义零镜头学习（GZSL）。由于源域和目标域是不相交的并且可能具有不相关的类，因此ZSL和GZSL通常会遭受投影域移位的问题。此外，原型的一些语义嵌入非常相似，这使得识别的判别性降低了。为了规避这些问题，在本文中，我们提出了一种新的方法，称为学习区分域不变式原型（DDIP）。在DDIP中，目标域和源域都被组合并投影到一个超球形空间中，该空间由正则化字典学习自动学习。此外，对潜在的超球面空间采用正交约束，以确保所有类别的原型（包括可见类别和不可见类别）彼此正交，以使它们更具区分性。在GZSL和标准ZSL设置上对四个流行的基准和大规模数据集进行了广泛的实验，结果表明我们的DDIP可以胜过最新方法。包括看到的类别和未看到的类别，它们彼此正交以使其更具区分性。在GZSL和标准ZSL设置上对四个流行的基准测试和大型数据集进行了广泛的实验，结果表明我们的DDIP可以胜过最新方法。包括看到的类别和未看到的类别，它们彼此正交以使其更具区分性。在GZSL和标准ZSL设置上对四个流行的基准和大规模数据集进行了广泛的实验，结果表明我们的DDIP可以胜过最新方法。