Evolution of Images with Diversity and Constraints Using a Generative Adversarial Network

Abstract

Evolutionary search has been extensively used to generate artistic images. Raw images have high dimensionality which makes a direct search for an image challenging. In previous work this problem has been addressed by using compact symbolic encodings or by constraining images with priors. Recent developments in deep learning have enabled a generation of compelling artistic images using generative networks that encode images with lower-dimensional latent spaces. To date this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles. There is currently no work which uses search in this latent space to generate images scoring high or low aesthetic measures. In this paper we use evolutionary methods to search for images in two datasets, faces and butterflies, and demonstrate the effect of optimising aesthetic feature scores in one or two dimensions. The work gives a preliminary indication of which feature measures promote the most interesting images and how some of these measures interact.

Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

“…Due its low cost, AIG provides novel opportunities for a wide range of applications, including health-care (Nie et al 2017), advertising (Neumann, Pyromallis, and Alexander 2018), and user analytics for human computer interaction (HCI) and design purposes (Salminen et al 2019a). However, despite the far-reaching interest in AIG among academia and across industries, there is scant research on evaluating the suitability of the generated images for practical use in deployed systems.…”