Generative Process

Generative processes are computational methods that create new data instances resembling a training dataset, aiming to understand and replicate the underlying data distribution. Current research heavily focuses on diffusion models, leveraging stochastic differential equations or ordinary differential equations to iteratively refine noisy data into realistic samples, often guided by textual or visual prompts. This field is significant for its applications in diverse areas like image generation, drug discovery, and robotic control, driving advancements in both fundamental AI research and practical technological solutions.