  New categories can be discovered by transforming semantic features into
synthesized visual features without corresponding training samples in zero-shot
image classification. Although significant progress has been made in generating
high-quality synthesized visual features using generative adversarial networks,
guaranteeing semantic consistency between the semantic features and visual
features remains very challenging. In this paper, we propose a novel zero-shot
learning approach, GAN-CST, based on class knowledge to visual feature learning
to tackle the problem. The approach consists of three parts, class knowledge
overlay, semi-supervised learning and triplet loss. It applies class knowledge
overlay (CKO) to obtain knowledge not only from the corresponding class but
also from other classes that have the knowledge overlay. It ensures that the
knowledge-to-visual learning process has adequate information to generate
synthesized visual features. The approach also applies a semi-supervised
learning process to re-train knowledge-to-visual model. It contributes to
reinforcing synthesized visual features generation as well as new category
prediction. We tabulate results on a number of benchmark datasets demonstrating
that the proposed model delivers superior performance over state-of-the-art
approaches.
