  This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room interiors, birds, flowers and faces. The key innovation of our work is
to allow back-propagation of the loss function w.r.t. the labels (randomly
assigned to each generated images) to the generator from the discriminator.
With the feedback from the label information, the generator is able to learn
faster and achieve better generated image quality. Empirically, we show that
the proposed ARTGAN is capable to create realistic artwork, as well as generate
compelling real world images that globally look natural with clear shape on
CIFAR-10.
