Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration.

ABSTRACT: Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user's preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance

SUBMITTER: Tejeda-Ocampo C 

PROVIDER: S-EPMC7823808 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature