Diffusion Models: How do They Diffuse?
Diffusion Models
The name "diffusion models" in machine learning was derived from the statistical concept of diffusion processes.
What is that statistical concept?
In natural sciences, diffusion is the process by which particles spread from areas of high concentration to areas of low concentration over time, often described by the diffusion equation in physics and mathematics.
Reaction-diffusion is an excellent example of this.
Reaction-Diffusion
Reaction-Diffusion is quite a complicated process; if you want to read the mathematical logic, you can visit the RD master Karl Sims' website:
Let's start with a simple analogy:
Reaction-diffusion systems are a way to describe how things change and move around, especially when you're talking about chemicals. Imagine you have a couple of different paints on a piece of paper, and they start to mix and create new colors – that's like the "reaction" part. The paint blots don't just stay in one spot; they spread out and blend – that spreading is like the "diffusion" part.
So, these systems are just a set of rules that tell us how these processes happen: how the chemicals react with each other to make new stuff and how they move around or spread out.
This can describe a lot of different things in nature, such as how patterns form on animal skins, how pollution spreads in the environment, and lots of other situations where stuff is reacting and moving at the same time!
Why is this useful?
Well, you can design shoes with them!
