Diffusion model

In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. These models are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process.

Diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. For example, an image generation model would start with a random noise image and then, after having been trained reversing the diffusion process on natural images, the model would be able to generate new natural images. A recent example of this is OpenAI's text-to-image model DALL-E 2, which uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image.

See also

• Diffusion process
• Markov chain
• Variational inference
• Variational autoencoder