Print Email Facebook Twitter Controlled Modification of Generated (Style)GAN Latent Vectors Title Controlled Modification of Generated (Style)GAN Latent Vectors Author Liu, Yifei (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Viering, Tom (graduation committee) Tax, David (mentor) Degree granting institution Delft University of Technology Date 2019-06-28 Abstract StyleGAN is a neural network architecture that is able to generate photo-realistic images. The diversity of generated images are ensured by latent vectors. These latent vectors encodes important features of generated images. They provide us insight-full information about properties of image generation in StyleGAN, which may also occur similarly in other neural network architectures. Using pre-trained StyleGAN models, we have conducted several experiments to show properties on StyleGAN based on results of style modification. The experiments have shown the influence of noise and styles in StyleGAN and how latent vectors can be manipulated in generated images. To reference this document use: http://resolver.tudelft.nl/uuid:01fcea28-7477-45d8-baa2-71b29bb07fc2 Part of collection Student theses Document type bachelor thesis Rights © 2019 Yifei Liu Files PDF BEP_paper_1_.pdf 12.58 MB Close viewer /islandora/object/uuid:01fcea28-7477-45d8-baa2-71b29bb07fc2/datastream/OBJ/view