  We propose a novel unsupervised generative model that learns to disentangle
object identity from other low-level aspects in class-imbalanced data. We first
investigate the issues surrounding the assumptions about uniformity made by
InfoGAN, and demonstrate its ineffectiveness to properly disentangle object
identity in imbalanced data. Our key idea is to make the discovery of the
discrete latent factor of variation invariant to identity-preserving
transformations in real images, and use that as a signal to learn the
appropriate latent distribution representing object identity. Experiments on
both artificial (MNIST, 3D cars, 3D chairs, ShapeNet) and real-world
(YouTube-Faces) imbalanced datasets demonstrate the effectiveness of our method
in disentangling object identity as a latent factor of variation.
