Denoising Diffusion Probabilistic Models
The paper introduces denoising diffusion probabilistic models (DDPMs), a class of latent-variable generative models trained to reverse a fixed Gaussian noising process. It establishes a connection between diffusion models and denoising score matching with Langevin dynamics, and proposes a simplified, reweighted training objective. The resulting models produce high-quality image samples, achieving competitive log-likelihoods and a strong FID on CIFAR-10.