The Need, Benefits, and Applications of Diffusion Models
Discover the key benefits and diverse applications of diffusion models, including image generation, data augmentation, and anomaly detection. This lesson covers the stable training advantages of diffusion models and their limitations, helping you understand when and how to use them effectively in various generative tasks.
We'll cover the following...
We’ve learned about what diffusion models are and how they’re used in previous lessons. Now, let’s look at their benefits, wide-ranging applications, and inherent limitations.
Benefits over other generative models
There are several benefits of diffusion models over the other generative models. Let’s discuss some of them:
In the context of training stability, diffusion models exhibit inherent robustness, mitigating challenges associated with unstable learning dynamics GANs face.
The progressive nature of diffusion, where samples are generated through a series of controlled diffusion steps, contributes to a stable training process.
The diffusion process allows for fine-grained control over the generation time steps. ...