  Generative Adversarial Networks (GANs) triggered an increased interest in
problem of image generation due to their improved output image quality and
versatility for expansion towards new methods. Numerous GAN-based works attempt
to improve generation by architectural and loss-based extensions. We argue that
one of the crucial points to improve the GAN performance in terms of realism
and similarity to the original data distribution is to be able to provide the
model with a capability to learn the spatial structure in data. To that end, we
propose the DeshuffleGAN to enhance the learning of the discriminator and the
generator, via a self-supervision approach. Specifically, we introduce a
deshuffling task that solves a puzzle of randomly shuffled image tiles, which
in turn helps the DeshuffleGAN learn to increase its expressive capacity for
spatial structure and realistic appearance. We provide experimental evidence
for the performance improvement in generated images, compared to the baseline
methods, which is consistently observed over two different datasets.
