How to explore the space of Art images? It is quite challenging to represent complex shapes and structures of the images.
In this work, we employ Generative Adversarial Networks (GANs) that are trained to produce creative abstract Art images using an architecture known as Creative Adversarial Networks (CANs), then, we use 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.
Based on: Hall, O., Yaman, A. Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_13 (**Best paper award**)