  VAEs (Variational AutoEncoders) have proved to be powerful in the context of
density modeling and have been used in a variety of contexts for creative
purposes. In many settings, the data we model possesses continuous attributes
that we would like to take into account at generation time. We propose in this
paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational
AutoEncoder architecture and its generalizations which allows a fine control on
the embedding of the data into the latent space. When augmenting the VAE loss
with this regularization, changes in the learned latent space reflects changes
of the attributes of the data. This deeper understanding of the VAE latent
space structure offers the possibility to modulate the attributes of the
generated data in a continuous way. We demonstrate its efficiency on a
monophonic music generation task where we manage to generate variations of
discrete sequences in an intended and playful way.
