Abstract
Neural Style Transfer, which is a machine learning optimization technique, is designed to generate images based on a particular painting style by analyzing numerous images to extract an artist’s style characteristics.More specifically, generated images are processed through a data archive in order to systematically apply the visual characteristics and styles of famous painters and thus generate new images that reflect those styles. In this study, based on our role as user experience (UX) designers, we attempt to ascertain the effectiveness of such style additions in order to create an advanced archive within which users can apply a variety of artists’ styles to different contents and thus generate unique images. This “artwork as visualization” process arranges generated images for each artwork based on different artists’ styles, thus allowing archive users to experience images that evoke the artistic feeling that is most suited to their intentions.
The Image Data Archive for Neural Style Transfer (yinteraction-design.com)
