Generalized Diffusion

Generalized diffusion models represent a powerful class of generative models extending beyond the traditional Gaussian noise-based approach. Current research focuses on broadening their applicability to diverse data types (images, 3D models, graphs, atomic structures) and tasks (generation, reconstruction, adaptation), often employing variations of diffusion processes and incorporating advanced architectures like UNets and transformers. This versatility is driving significant advancements in areas such as image denoising, test-time adaptation for improved robustness in machine learning, and the generation of complex structures in materials science, highlighting the growing importance of generalized diffusion in various scientific and engineering domains.