Diffusion Models

Diffusion models are a class of deep learning models that generate high-quality data samples by iteratively refining random noise until it converges to a specific data distribution. As a rapidly evolving research area, diffusion models have gained significant attention in the tech community for their potential to revolutionize applications such as image and video generation, data augmentation, and generative art, offering a promising alternative to traditional generative adversarial networks (GANs) with improved stability and mode coverage.