RenderBEGAN: Adversarial Generative Domain Adaptation
Supervised training methods require a lot of labeled training data which often does not exist in sufficient amounts as the data has to be annoxated manually - a laborious task. This thesis describes a novel approach of unsupervised domain adaptation which turns labeled simulated data samples into labeled realistic data samples by using unlabeled real data samples for training. It is shown that the proposed model is able to generate realistic labeled images of human faces out of simulated face models generated from the Basel Face Model.
Master of Science (M.Sc.)