  Learning useful representations of complex data has been the subject of
extensive research for many years. With the diffusion of Deep Neural Networks,
Variational Autoencoders have gained lots of attention since they provide an
explicit model of the data distribution based on an encoder/decoder
architecture which is able to both generate images and encode them in a
low-dimensional subspace. However, the latent space is not easily interpretable
and the generation capabilities show some limitations since images typically
look blurry and lack details. In this paper, we propose the Introspective
Variational Classifier (IntroVAC), a model that learns interpretable latent
subspaces by exploiting information from an additional label and provides
improved image quality thanks to an adversarial training strategy.We show that
IntroVAC is able to learn meaningful directions in the latent space enabling
fine-grained manipulation of image attributes. We validate our approach on the
CelebA dataset.
