  Generative Adversarial Networks (GANs) have become the most used networks
towards solving the problem of image generation. Self-supervised GANs are later
proposed to avoid the catastrophic forgetting of the discriminator and to
improve the image generation quality without needing the class labels. However,
the generalizability of the self-supervision tasks on different GAN
architectures is not studied before. To that end, we extensively analyze the
contribution of a previously proposed self-supervision task, deshuffling of the
DeshuffleGANs in the generalizability context. We assign the deshuffling task
to two different GAN discriminators and study the effects of the task on both
architectures. We extend the evaluations compared to the previously proposed
DeshuffleGANs on various datasets. We show that the DeshuffleGAN obtains the
best FID results for several datasets compared to the other self-supervised
GANs. Furthermore, we compare the deshuffling with the rotation prediction that
is firstly deployed to the GAN training and demonstrate that its contribution
exceeds the rotation prediction. We design the conditional DeshuffleGAN called
cDeshuffleGAN to evaluate the quality of the learnt representations. Lastly, we
show the contribution of the self-supervision tasks to the GAN training on the
loss landscape and present that the effects of these tasks may not be
cooperative to the adversarial training in some settings. Our code can be found
at https://github.com/gulcinbaykal/DeshuffleGAN.
