Algorithmic Aesthetics: Generative Adversarial Networks for Aesthetic Evaluation

Abstract

This paper explores the potential of Generative Adversarial Networks (GANs) to contribute to the field of aesthetic evaluation. The central argument posits that GANs, with their ability to generate novel outputs, can be leveraged to develop a formalized understanding of aesthetic quality. The proposed approach utilizes a two-GAN system: a generator tasked with creating artistic outputs, and a discriminator trained to evaluate their aesthetic merit based on human-provided ratings. The core mechanism involves an adversarial training process where the generator attempts to deceive the discriminator, ultimately leading to the generation of outputs deemed aesthetically pleasing by the trained discriminator. This work highlights the subjective nature of aesthetic judgment while simultaneously proposing a novel methodology for capturing and potentially replicating aesthetic preferences through machine learning. The research investigates the feasibility of training a 'discriminator' GAN to learn a quantifiable metric for aesthetic quality, representing a significant step towards creating intelligent creative AI systems.