  Learning disentangled and interpretable representations is an important step
towards accomplishing comprehensive data representations on the manifold. In
this paper, we propose a novel representation learning algorithm which combines
the inference abilities of Variational Autoencoders (VAE) with the
generalization capability of Generative Adversarial Networks (GAN). The
proposed model, called InfoVAEGAN, consists of three networks~: Encoder,
Generator and Discriminator. InfoVAEGAN aims to jointly learn discrete and
continuous interpretable representations in an unsupervised manner by using two
different data-free log-likelihood functions onto the variables sampled from
the generator's distribution. We propose a two-stage algorithm for optimizing
the inference network separately from the generator training. Moreover, we
enforce the learning of interpretable representations through the maximization
of the mutual information between the existing latent variables and those
created through generative and inference processes.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition