Diffusion Models in Generative Modeling

This lecture delves into diffusion models in generative modeling, starting with an overview of generative models and diving into the intuition and mathematics behind diffusion models. The instructor explains how these models estimate probability distributions of data, generate new data points, and evaluate probabilities. The lecture covers the challenges of modeling complex high-dimensional distributions, the use of deep neural networks, and the concept of denoising diffusion models. It also explores the transition to continuous time and the benefits of using stochastic differential equations. The discussion extends to controlling generation, evaluating probabilities, and the potential of diffusion models in various applications.