Foundations and Architectures of Generative AI Models

Abstract: This chapter delves into the theoretical and computational foundations that enable generative systems to learn and create. It explains latent space representations, neural network topologies, and optimization techniques that underpin state-of-the-art architectures. Core models-including Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models-are analyzed in terms of structure, training mechanisms, and comparative performance. The role of transformers and large language models in advancing multimodal generation is emphasized. Readers gain a rigorous understanding of how algorithms transform data patterns into new and coherent outputs.