Diffusion models, a class of generative models in machine learning, are revolutionizing the field by offering a unique approach to understanding and replicating data distributions. Unlike traditional models like VAEs and GANs, diffusion models add noise to the data and then iteratively refine it through a denoising process. This gradual transformation allows them to generate high-quality data, making them highly versatile in tasks like image synthesis and text generation. With their remarkable capabilities, diffusion models are shaping a new era in generative machine learning. This summary sets the stage for exploring their principles, architecture, applications, challenges, and future potential, inviting us on an exciting journey into the world of diffusion models. Read more..
Diffusion Models in Machine Learning
