Architecture of Diffusion Models
The architecture of diffusion models typically involves two main components:
Forward Diffusion Process
In this process, noise is incrementally added to the data over a series of steps. This is akin to a Markov chain where each step slightly degrades the data by adding Gaussian noise.
Mathematically, this can be represented as:
[Tex]q(x_t | x_{t-1}) = \mathcal{N}(x_t; \sqrt{\alpha_t} x_{t-1}, (1 – \alpha_t)I)[/Tex]
where,
- [Tex]x_t[/Tex] is the noisy data at step t,
- [Tex]\alpha_t[/Tex] controls the amount of noise added.
Reverse Diffusion Process
The reverse process aims to reconstruct the original data by denoising the noisy data in a series of steps, reversing the forward diffusion.
This is typically modelled using a neural network that predicts the noise added at each step:
[Tex]p_\theta(x_{t-1} | x_t) = \mathcal{N}(x_{t-1}; \mu_\theta(x_t, t), \sigma_\theta(x_t, t)) [/Tex]
where,
- [Tex]\mu_\theta[/Tex] and [Tex]\sigma_\theta[/Tex] are learned parameters.
What are Diffusion Models?
Diffusion models are a powerful class of generative models that have gained prominence in the field of machine learning and artificial intelligence. They offer a unique approach to generating data by simulating the diffusion process, which is inspired by physical processes such as heat diffusion. This article delves into the diffusion model, exploring its architecture, working principles, applications, and advantages.
Table of Content
- Understanding Diffusion Models
- Key Components of Diffusion Models
- Architecture of Diffusion Models
- Working Principle of Diffusion Models
- Training Diffusion Models
- Applications of Diffusion Models
- Advantages of Diffusion Models
- Limitations of Diffusion Models
- Conclusion