Generative Diffusion

Generative diffusion models are a powerful class of probabilistic models that generate data by reversing a diffusion process, transforming noise into structured samples. Current research focuses on extending these models to conditional generation, improving efficiency through techniques like single-step diffusion and minimax optimization, and applying them to diverse domains including image restoration, 3D scene generation, and sequential recommendation. This rapidly evolving field is significantly impacting various scientific disciplines and practical applications by enabling high-fidelity data generation, improved data analysis, and the development of novel algorithms for tasks such as anomaly detection and medical image analysis.