  Zero-shot learning (ZSL) is to handle the prediction of those unseen classes
that have no labeled training data. Recently, generative methods like
Generative Adversarial Networks (GANs) are being widely investigated for ZSL
due to their high accuracy, generalization capability and so on. However, the
side information of classes used now is limited to text descriptions and
attribute annotations, which are in short of semantics of the classes. In this
paper, we introduce a new generative ZSL method named KG-GAN by incorporating
rich semantics in a knowledge graph (KG) into GANs. Specifically, we build upon
Graph Neural Networks and encode KG from two views: class view and attribute
view considering the different semantics of KG. With well-learned semantic
embeddings for each node (representing a visual category), we leverage GANs to
synthesize compelling visual features for unseen classes. According to our
evaluation with multiple image classification datasets, KG-GAN can achieve
better performance than the state-of-the-art baselines.
