Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. The present works on ZSL mainly focus on the mapping of learning semantic space to visual space. It encounters many challenges that obstruct the progress of ZSL research. First, the representation of the semantic feature is inadequate to represent all features of the categories. Second, the domain drift problem still exists during the transfer from semantic space to visual space. In this paper, we introduce knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features. Abundant experimental results from two benchmark datasets of ZSL show that the proposed approach has a consistent improvement.

翻译：零热学习(ZSL)是一项新兴研究,旨在用极少的培训数据解决分类问题。目前,ZSL的工作主要侧重于将学习语义空间绘图到视觉空间。它遇到许多阻碍ZSL研究进展的挑战。首先,语义特征的表述不足以代表各类的所有特征。第二,从语义空间向视觉空间的转移期间,仍然存在着域流问题。在本文件中,我们引入知识共享(KS)以丰富语义特征的表述。根据KS,我们应用基因对抗网络从非常接近真实视觉特征的语义特征产生假的视觉特征。ZSL的两个基准数据集所产生的大量实验结果显示,拟议的方法有持续改进。