Self-Supervised GANs – Towards Data Science

If you aren't familiar with Generative Adversarial Networks (GANs), they are a massively popular generative modeling technique formed by pitting two Deep Neural Networks, a generator and a discriminator, against each other. This adversarial loss has sparked the interest of many Deep Learning and Artificial Intelligence researchers. However, despite the beauty of the GAN formulation and the eye-opening results of the state-of-the-art architectures, GANs are generally very difficult to train. One of the best ways to get better results with GANs are to provide class labels. This is the basis of the conditional-GAN model.