Generative Adversarial Nets

This paper introduces a new framework called Generative Adversarial Networks (GANs). In a GAN, two neural networks compete against each other: a generator that tries to create realistic images, and a discriminator that tries to distinguish between real and generated images. Through this adversarial process, the generator learns to produce increasingly realistic images that can fool the discriminator, ultimately leading to the generation of high-quality synthetic images. GANs have since become a foundational technique in generative modeling and image synthesis.