  Generative adversarial networks are the state of the art approach towards
learned synthetic image generation. Although early successes were mostly
unsupervised, bit by bit, this trend has been superseded by approaches based on
labelled data. These supervised methods allow a much finer-grained control of
the output image, offering more flexibility and stability. Nevertheless, the
main drawback of such models is the necessity of annotated data. In this work,
we introduce an novel framework that benefits from two popular learning
techniques, adversarial training and representation learning, and takes a step
towards unsupervised conditional GANs. In particular, our approach exploits the
structure of a latent space (learned by the representation learning) and
employs it to condition the generative model. In this way, we break the
traditional dependency between condition and label, substituting the latter by
unsupervised features coming from the latent space. Finally, we show that this
new technique is able to produce samples on demand keeping the quality of its
supervised counterpart.
