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Learning disentangled and interpretable representations is an important steptowards accomplishing comprehensive data representations on the manifold. Inthis paper, we propose a novel representation learning algorithm which combinesthe inference abilities of Variational Autoencoders (VAE) with thegeneralization capability of Generative Adversarial Networks (GAN). Theproposed model, called InfoVAEGAN, consists of three networks~: Encoder,Generator and Discriminator. InfoVAEGAN aims to jointly learn discrete andcontinuous interpretable representations in an unsupervised manner by using twodifferent data-free log-likelihood functions onto the variables sampled fromthe generator's distribution. We propose a two-stage algorithm for optimizingthe inference network separately from the generator training. Moreover, weenforce the learning of interpretable representations through the maximizationof the mutual information between the existing latent variables and thosecreated through generative and inference processes.

InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood