A new SotA for generative modelling -- Denoising Diffusion Probabilistic Models

Generative models create latent representations, which distil information from big data in order to generate realistic and novel data points. In the long term, these models could be vital in developing accurate world models, as well as learning categorical and continuous features of a dataset in an unsupervised way. Currently, generative models are demonstrating their value in a variety of downstream tasks such as inpainting, super-resolution, and generating continuous exploration spaces for reinforcement learning. Generative Adversarial Networks (GANs) have represented the state of the art (SotA) for some time, however recently OpenAI has published results that make a strong case for a new era of Denoising Diffusion Probabilistic models dominating generative SotA applications. In this article, I shall introduce the theory behind this method and describe the contributions which have enabled this relatively unstudied technique to topple GANs.