  The task of zero-shot learning (ZSL) requires correctly predicting the label
of samples from classes which were unseen at training time. This is achieved by
leveraging side information about class labels, such as label attributes or
word embeddings. Recently, attention has shifted to the more realistic task of
generalized ZSL (GZSL) where test sets consist of seen and unseen samples.
Recent approaches to GZSL have shown the value of generative models, which are
used to generate samples from unseen classes. In this work, we incorporate an
additional source of side information in the form of a relation graph over
labels. We leverage this graph in order to learn a set of prior distributions,
which encourage an aligned variational autoencoder (VAE) model to learn
embeddings which respect the graph structure. Using this approach we are able
to achieve improved performance on the CUB and SUN benchmarks over a strong
baseline.
