  Disentangling factors of variation within data has become a very challenging
problem for image generation tasks. Current frameworks for training a
Generative Adversarial Network (GAN), learn to disentangle the representations
of the data in an unsupervised fashion and capture the most significant factors
of the data variations. However, these approaches ignore the principle of
content and style disentanglement in image generation, which means their
learned latent code may alter the content and style of the generated images at
the same time. This paper describes the Style and Content Disentangled GAN
(SC-GAN), a new unsupervised algorithm for training GANs that learns
disentangled style and content representations of the data. We assume that the
representation of an image can be decomposed into a content code that
represents the geometrical information of the data, and a style code that
captures textural properties. Consequently, by fixing the style portion of the
latent representation, we can generate diverse images in a particular style.
Reversely, we can set the content code and generate a specific scene in a
variety of styles. The proposed SC-GAN has two components: a content code which
is the input to the generator, and a style code which modifies the scene style
through modification of the Adaptive Instance Normalization (AdaIN) layers'
parameters. We evaluate the proposed SC-GAN framework on a set of baseline
datasets.
