  In computer vision, disentangling techniques aim at improving latent
representations of images by modeling factors of variation. In this paper, we
propose DualDis, a new auto-encoder-based framework that disentangles and
linearizes class and attribute information. This is achieved thanks to a
two-branch architecture forcing the separation of the two kinds of information,
accompanied by a decoder for image reconstruction and generation. To
effectively separate the information, we propose to use a combination of
regular and adversarial classifiers to guide the two branches in specializing
for class and attribute information respectively. We also investigate the
possibility of using semi-supervised learning for an effective disentangling
even using few labels. We leverage the linearization property of the latent
spaces for semantic image editing and generation of new images. We validate our
approach on CelebA, Yale-B and NORB by measuring the efficiency of information
separation via classification metrics, visual image manipulation and data
augmentation.
