Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models

arXiv:2403.19620v1 Announce Type: cross
Abstract: 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 …

abstract adversarial art arxiv collaborative cs.ai cs.cv cs.hc cs.lg cs.ne deep generative models evolution gans generate generative generative adversarial networks generative models however image image generation image generation process images interactive networks process quality sampling space success type