Breaking Ground with GANs

This chapter explores the breakthrough moment in the development of Generative Adversarial Networks (GANs), emphasizing the balance between focused technical work and creative exploration. It details the mechanics of GANs, where a generator creates images and a discriminator evaluates their authenticity, fostering a competitive learning environment. The chapter also highlights the practical applications of GANs, particularly in reducing dependency on labeled data, which is especially beneficial in fields like medicine.