Generative Adversarial Networks (GANs) are a popular class of generative deep learning models, commonly used for image generation. They consist of a pair of dueling neural networks, called the discriminator and the generator. The discriminator's task is to distinguish real images from generated (fake) ones, while the generator network tries to fool the discriminator by generating more and more realistic images. If the generator is however too easy or too hard to fool, it might fail to provide useful learning signal for the generator, therefore training GANs is usually considered a difficult task.