Generative Adversarial Networks (GANs) for Creative Applications: Exploring Art and Music Generation

Abstract

In this exploration, the focus is on the intersection of artificial intelligence and creative expression, particularly within the domains of art and music. Generative Adversarial Networks (GANs), a class of machine learning algorithms, are at the forefront of this investigation. These networks consist of two neural networks, a generator, and a discriminator, engaged in an adversarial training process. The research aims to unravel the unique capabilities of GANs in fostering creativity. By employing adversarial learning, GANs have demonstrated the ability to produce artworks and musical compositions that exhibit a level of novelty and aesthetic appeal. This study involves a comprehensive analysis of the intricate interplay between the generator and discriminator networks, seeking to understand how GANs can capture and replicate the nuances of artistic styles and musical genres. Moreover, the research explores the implications of integrating machine-generated creativity into the broader artistic landscape.