Lecture
This lecture covers Data Denoising Diffusion Models, which consist of a forward diffusion process adding noise to input and a reverse denoising process generating data. It explains the formal definitions of the forward and reverse processes, the diffusion kernel, and the training objectives. The lecture also discusses the parameterization of the denoising model, progressive distillation for faster sampling, classifier guidance and classifier-free guidance techniques. It concludes with the challenges of applying diffusion to text and summarizes the key points of diffusion for data generation and denoising.