  Disentangling factors of variation has become a very challenging problem on
representation learning. Existing algorithms suffer from many limitations, such
as unpredictable disentangling factors, poor quality of generated images from
encodings, lack of identity information, etc. In this paper, we propose a
supervised learning model called DNA-GAN which tries to disentangle different
factors or attributes of images. The latent representations of images are
DNA-like, in which each individual piece (of the encoding) represents an
independent factor of the variation. By annihilating the recessive piece and
swapping a certain piece of one latent representation with that of the other
one, we obtain two different representations which could be decoded into two
kinds of images with the existence of the corresponding attribute being
changed. In order to obtain realistic images and also disentangled
representations, we further introduce the discriminator for adversarial
training. Experiments on Multi-PIE and CelebA datasets finally demonstrate that
our proposed method is effective for factors disentangling and even overcome
certain limitations of the existing methods.
