Generative Models: An Overview of GANs and Diffusion Models

Abstract

Generative models have emerged as a powerful class of machine learning techniques capable of creating realistic data samples such as images, audio, video, and text. Among the most influential generative approaches are Generative Adversarial Networks (GANs) and Diffusion Models. GANs introduced a competitive learning framework between generator and discriminator networks, leading to high-quality image synthesis. However, training instability and mode collapse have limited their usability. Recently, diffusion models have gained attention due to their stable training process and impressive generative quality by modeling data through gradual noise addition and removal. This paper presents a comprehensive review of GANs and diffusion models, discussing their architectures, working principles, improvements, applications, advantages, and limitations. A comparative analysis is also provided to understand their differences and practical usability in real-world scenarios.

KEYWORDS: Generative Models, GANs, Diffusion Models, Deep Learning, Image Synthesis, Adversarial Learning, Probabilistic Models