Generative Adversarial Nets

This paper introduces Generative Adversarial Networks (GANs), a framework where two neural networks compete: a generator that creates synthetic data, and a discriminator that evaluates the authenticity of the generated data. Through this adversarial process, the generator learns to produce increasingly realistic data, while the discriminator becomes better at distinguishing real from fake data. GANs have become a foundational technique in AI for generating new content, including images, and have influenced logo and brand identity generation tools.