Generative Deep Learning

This chapter focuses on generative deep learning, with a core emphasis on Generative Adversarial Network (GAN). It first lays the groundwork for deep learning by introducing its three paradigms: unsupervised learning, supervised learning, and reinforcement learning, along with the differences between deep learning and traditional machine learning in terms of data volume, hardware dependence, and other aspects. Subsequently, it introduces GAN, proposed in 2014, elaborating on their dual-module framework consisting of a generator (which produces data resembling real samples) and a discriminator (which distinguishes between real and fake data), as well as the adversarial training mechanism based on Nash equilibrium. Meanwhile, it supplements relevant foundational concepts such as gradient descent and Kullback-Leibler (KL) divergence, details the iterative training process of GAN—“training the discriminator first, then the generator”—and provides a specific application code example using the MNIST handwritten digit dataset. Finally, it analyzes the advantages of GAN, such as eliminating the need for complex Markov chains and automatically fitting data distributions, as well as their limitations including mode collapse and convergence difficulties; it also introduces derivative models in the “GAN Zoo” and prospects their future expansion into multiple domains and integration with advanced technologies.