Generative Models - Architectures and Applications of GANs, VAEs, and Diffusion Models

Generative models have emerged as one of the most powerful paradigms in modern artificial intelligence, enabling machines to learn complex data distributions and generate new, realistic samples. This article presents an overview of three major classes of generative models-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models-focusing on their architectures, theoretical foundations, and applications. Furthermore, we provide a prototype implementation example to illustrate practical aspects of training generative models.