GANAesthetic : An experience of interactively exploring aesthetically pleasing images and incorporating the human perception of beauty to discover aesthetic latent dimensions
T.H. Nguyen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.D. Lomas – Mentor (TU Delft - Form and Experience)
Ujwal Gadiraju – Mentor (TU Delft - Web Information Systems)
Willem Van Der Maden – Mentor (TU Delft - Form and Experience)
Garrett Allen – Mentor (TU Delft - Web Information Systems)
D.H.J. Epema – Graduation committee member (TU Delft - Data-Intensive Systems)
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Abstract
Despite the fact that climate change is becoming increasingly dangerous and prevalent, there is still a lack of public engagement. This can be explained by the fact that the media portrays climate change as an abstract concept. The message can be more effectively communicated through visual art because it is more likely to invoke emotional responses in individuals. By including human perception and rating data, the generative adversarial neural network (GAN) produces better image output. Therefore, this paper explores methods for using the human perception of beauty in order to improve StyleGAN2 outputs. In GANAesthetic, UI sliders allow users to explore satellite images interactively, that is, visually appealing satellite images generated from StyleGAN2. The GANAesthetic was determined to be the most appropriate methodology for the study. The choice of GANAesthetic over other approaches will be explained in this paper, as well as its implementation. The paper will also describe an experiment to discover aesthetic latent dimensions.