An Improved Self-supervised GAN via Adversarial Training

Understand

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.