Discrete Denoising Diffusion

Discrete denoising diffusion models are generative models that learn to reverse a process of progressively adding noise to discrete data, such as images, graphs, or text, to generate new samples. Current research focuses on improving sampling efficiency through techniques like optimized time discretization and unified frameworks for both discrete and continuous-time processes, as well as enhancing model performance and addressing issues like privacy preservation and explanation generation. These advancements are significant for various applications, including synthetic data generation, graph analysis, and improving the explainability and trustworthiness of machine learning models.