Generative Diffusion From An Action Principle

The field of Generative Artificial Intelligence has witnessed remarkable progress in recent years, fueled by the advent of novel deep learning techniques. Among these advancements, diffusion-based models have emerged as a promising paradigm for generating high-quality, high-dimensional, diverse, and coherent data samples. These models leverage principles from non-equilibrium statistical mechanics to effectively reconstruct the underlying probability distribution from which a training data set was sampled. The central idea behind diffusion models is reverse diffusion. These models gradually add noise to a given data set and observe how the data vectors evolve over time.