  We present a meta-learning based generative model for zero-shot learning
(ZSL) towards a challenging setting when the number of training examples from
each \emph{seen} class is very few. This setup contrasts with the conventional
ZSL approaches, where training typically assumes the availability of a
sufficiently large number of training examples from each of the seen classes.
The proposed approach leverages meta-learning to train a deep generative model
that integrates variational autoencoder and generative adversarial networks. We
propose a novel task distribution where meta-train and meta-validation classes
are disjoint to simulate the ZSL behaviour in training. Once trained, the model
can generate synthetic examples from seen and unseen classes. Synthesize
samples can then be used to train the ZSL framework in a supervised manner. The
meta-learner enables our model to generates high-fidelity samples using only a
small number of training examples from seen classes. We conduct extensive
experiments and ablation studies on four benchmark datasets of ZSL and observe
that the proposed model outperforms state-of-the-art approaches by a
significant margin when the number of examples per seen class is very small.
