Applications and challenges of GAN in AI-powered artistry
Shuxue Ran · Applied and Computational Engineering · 2024
In the evolving landscape of artificial intelligence (AI), Generative Adversarial Network (GAN), introduced in 2014 by Goodfellow and team, has emerged as a vital pillar in deep learning. Designed around the concept of adversarial learning, GAN consists of a generator and a discriminator working in tandem, with the former creating counterfeit data samples and the latter distinguishing between genuine and counterfeit ones. The paper delves deep into GAN’s underlying architecture, its modified variants like DCGAN, WGAN, WGAN-GP, and CGAN, and its expansive applications in the realm of AI-powered artistry. Notably, applications like Stable Diffusion and NovelAI have demonstrated GAN’s proficiency in crafting visually stunning and diverse artistic outputs. However, this evolution isn’t without challenges. The ambiguities surrounding copyright ownership of AI-generated art and the potential disruption of the traditional art sector raise critical questions. As AI continues to redefine the boundaries of art, it’s imperative to ensure its responsible and beneficial integration into society.