  We propose to improve unconditional Generative Adversarial Networks (GAN) by
training the self-supervised learning with the adversarial process. In
particular, we apply self-supervised learning via the geometric transformation
on input images and assign the pseudo-labels to these transformed images. (i)
In addition to the GAN task, which distinguishes data (real) versus generated
(fake) samples, we train the discriminator to predict the correct pseudo-labels
of real transformed samples (classification task). Importantly, we find out
that simultaneously training the discriminator to classify the fake class from
the pseudo-classes of real samples for the classification task will improve the
discriminator and subsequently lead better guides to train generator. (ii) The
generator is trained by attempting to confuse the discriminator for not only
the GAN task but also the classification task. For the classification task, the
generator tries to confuse the discriminator recognizing the transformation of
its output as one of the real transformed classes. Especially, we exploit that
when the generator creates samples that result in a similar loss (via
cross-entropy) as that of the real ones, the training is more stable and the
generator distribution tends to match better the data distribution. When
integrating our techniques into a state-of-the-art Auto-Encoder (AE) based-GAN
model, they help to significantly boost the model's performance and also
establish new state-of-the-art Fr\'echet Inception Distance (FID) scores in the
literature of unconditional GAN for CIFAR-10 and STL-10 datasets.
