Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Ole Hall, Anil Yaman
备注：
Preprint. The Version of Record of this contribution is to be published in the proceedings of the 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) 2024
Generative Adversarial Networks (GANs) have shown great success in generating high quality images and are thus used as one of the main approaches to generate art images. However, usually the image generation process involves sampling from the latent space of the learned art representations, allowing little control over the output. In this work, we first employ GANs that are trained to produce creative images using an architecture known as Creative Adversarial Networks (CANs), then, we employ an evolutionary approach to navigate within the latent space of the models to discover images. We use automatic aesthetic and collaborative interactive human evaluation metrics to assess the generated images. In the human interactive evaluation case, we propose a collaborative evaluation based on the assessments of several participants. Furthermore, we also experiment with an intelligent mutation operator that aims to improve the quality of the images through local search based on an aesthetic measure. We evaluate the effectiveness of this approach by comparing the results produced by the automatic and collaborative interactive evolution. The results show that the proposed approach can generate highly attractive art images when the evolution is guided by collaborative human feedback.
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
Southwestern University（西南大学）；Southern University of Science and Technology（南方科技大学）；Queen Mary University of London（伦敦玛丽女王大学）；IT University of Copenhagen（哥本哈根信息技术大学）
