  Conditional GANs are at the forefront of natural image synthesis. The main
drawback of such models is the necessity for labeled data. In this work we
exploit two popular unsupervised learning techniques, adversarial training and
self-supervision, and take a step towards bridging the gap between conditional
and unconditional GANs. In particular, we allow the networks to collaborate on
the task of representation learning, while being adversarial with respect to
the classic GAN game. The role of self-supervision is to encourage the
discriminator to learn meaningful feature representations which are not
forgotten during training. We test empirically both the quality of the learned
image representations, and the quality of the synthesized images. Under the
same conditions, the self-supervised GAN attains a similar performance to
state-of-the-art conditional counterparts. Finally, we show that this approach
to fully unsupervised learning can be scaled to attain an FID of 23.4 on
unconditional ImageNet generation.
