Neural Bauhaus Style Transfer
Sneha Mohanty, Xiaoni Cai, Anh Phuong Le
(Christian Benz, Prof. Dr.-Ing. Volker Rodehorst)
Whereas typical deep learning models only have discriminative capabilities - basically classifying or regressing images or pixels - Generative Adversarial Networks (GANs) are capable of generating, i.e. producing or synthesizing new images. A whole movement has emerged around the CycleGAN approach, which tries to apply the style of one image set (say the paintings of Van Gogh) onto another (say landscape photographs). The applicability of this approach for the transfer of Bauhaus style onto objects or buildings in images or whole images should be explored. At the end of the project a minor exploration on a seemingly different, but well-related problem takes place: In how far is the obtained GAN capable of augmenting a dataset of structural defect data.