  To overcome the absence of training data for unseen classes, conventional
zero-shot learning approaches mainly train their model on seen datapoints and
leverage the semantic descriptions for both seen and unseen classes. Beyond
exploiting relations between classes of seen and unseen, we present a deep
generative model to provide the model with experience about both seen and
unseen classes. Based on the variational auto-encoder with class-specific
multi-modal prior, the proposed method learns the conditional distribution of
seen and unseen classes. In order to circumvent the need for samples of unseen
classes, we treat the non-existing data as missing examples. That is, our
network aims to find optimal unseen datapoints and model parameters, by
iteratively following the generating and learning strategy. Since we obtain the
conditional generative model for both seen and unseen classes, classification
as well as generation can be performed directly without any off-the-shell
classifiers. In experimental results, we demonstrate that the proposed
generating and learning strategy makes the model achieve the outperforming
results compared to that trained only on the seen classes, and also to the
several state-of-the-art methods.
