What are generative adversarial networks (GANs)?

Generative adversarial networks (GANs) are indeed best described as a class of AI involving two neural networks that compete to generate data. In a typical GAN setup, there are two components: the generator and the discriminator. The generator creates new data instances, while the discriminator evaluates them against real data, determining whether each instance is real or fake. This process of competing and iteratively improving leads to the generation of highly realistic data, making GANs particularly useful in applications like image synthesis, video generation, and more.

Understanding the architecture of GANs is crucial to recognize their unique position in the AI landscape, characterized by this adversarial relationship that drives both networks toward better performance. This competition enables GANs to produce results that are often indistinguishable from real-world data, showcasing their powerful generative capabilities.