Structured Diffusion

Structured diffusion models are generative AI methods that leverage diffusion processes to learn and sample from complex probability distributions while incorporating prior knowledge or constraints about the data's underlying structure. Current research focuses on enhancing the efficiency and quality of image generation using these models, exploring architectures like white-box transformers and incorporating techniques such as adversarial training and energy-based discrimination for improved sample quality and control. These advancements are impacting diverse fields, including medical image analysis (e.g., tumor segmentation), material science (e.g., microstructure design), and the generation of counterfactual explanations for machine learning models, demonstrating the broad applicability of structured diffusion.