  Generative Adversarial Networks (GANs) are able to learn mappings between
simple, relatively low-dimensional, random distributions and points on the
manifold of realistic images in image-space. The semantics of this mapping,
however, are typically entangled such that meaningful image properties cannot
be controlled independently of one another. Conditional GANs (cGANs) provide a
potential solution to this problem, allowing specific semantics to be enforced
during training. This solution, however, depends on the availability of precise
labels, which are sometimes difficult or near impossible to obtain, e.g. labels
representing lighting conditions or describing the background. In this paper we
introduce a new formulation of the cGAN that is able to learn disentangled,
multivariate models of semantically meaningful variation and which has the
advantage of requiring only the weak supervision of binary attribute labels.
For example, given only labels of ambient / non-ambient lighting, our method is
able to learn multivariate lighting models disentangled from other factors such
as the identity and pose. We coin the method intra-class variation isolation
(IVI) and the resulting network the IVI-GAN. We evaluate IVI-GAN on the CelebA
dataset and on synthetic 3D morphable model data, learning to disentangle
attributes such as lighting, pose, expression, and even the background.
