  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.
