Power and Potential of Diffusion Models in Artificial Intelligence: Comprehensive Analysis
Introduction
Artificial Intelligence (AI) has become one of the most discussed and adopted technologies in recent years, and diffusion models are emerging as one of the most important new directions in this area. These models based on diffusion processes have shown amazing success in so many applications, such as image generation, text generation, drug discovery, and denoising. This post aims to give a thorough analysis of diffusion models, providing an introduction into the underlying principles and assessing their possible implications for the future of AI.
Understanding Diffusion Models
Diffusion models are a class of generative models that rely on the concept of diffusion processes — the description of how a system becomes changed over time due to random perturbations. The main idea behind diffusion models is developing some simple distribution, such as a Gaussian distribution, into the complex data distribution of interest, although the data used in practice may not always be simple. To achieve this, we use diffusion models in such a way that the system of interest is the data distribution, and random perturbations are modeled by using a stochastic differential equation (SDE). In general, one of the most important developments of this…
