Development and evaluation of new GAN models and estimation techniques

Over the recent years Generative Adversarial Models (GANs) have revolutionized the field of artificial intelligence (AI). Given a set of training data, the models can generate outcomes with striking similarities in properties to the training data. E.g. if the training data is a set of images, the models are able to generate new images with the same properties and look stunningly realistic. However the theoretical foundation of GANs is still immature and further understanding is needed. For example, GANs have been criticised to possibly only replicate a limited set of the properties in the training data.