  We address the problem of one-to-many mappings in supervised learning, where
a single instance has many different solutions of possibly equal cost. The
framework of conditional variational autoencoders describes a class of methods
to tackle such structured-prediction tasks by means of latent variables. We
propose to incentivise informative latent representations for increasing the
generalisation capacity of conditional variational autoencoders. To this end,
we modify the latent variable model by defining the likelihood as a function of
the latent variable only and introduce an expressive multimodal prior to enable
the model for capturing semantically meaningful features of the data. To
validate our approach, we train our model on the Cornell Robot Grasping
dataset, and modified versions of MNIST and Fashion-MNIST obtaining results
that show a significantly higher generalisation capability.
