Transfer learning with generative adversarial networks

Abstract

Generative Adversarial Networks (GANs) emerged in recent years as the undiscussed SotA for image synthesis. This model leverages the recent successes of convolutional networks in the field of computer vision to learn the probability distribution of image datasets. Following the first proposal of GANs, many developments and usages of the models have been proposed. This thesis aims to review the evolution of the model and use one of the most recent variations to generate realistic portrait images with a targeted set of features. The usage of this model will be applied in a transfer learning approach, discussing the advantages and disadvantages from standard approaches. Furthermore, classical and deep computer vision tools will be used to edit and confirm the results obtained from the GAN model.