In computer vision, image generation, and natural language processing, diffusion models are fundamental tools. They are utilized for functions such as image denoising, inpainting, super-resolution, and text generation, with the capability to train for the removal of Gaussian noise in blurred images. Notable examples of such models encompass denoising diffusion probabilistic models and noise conditioned score networks. In non-equilibrium thermodynamics, these models are instrumental in sampling from intricate probability distributions. They are further optimized through advanced methodologies like variational déduction[1] and stochastic gradient descent. In natural language processing, diffusion models are instrumental for text generation and summarization, mastering the hidden structure of text data to yield contextually appropriate text. Renowned research entities like OpenAI and Google[2] Imagen have pioneered various diffusion models for tasks related to image and text generation.
En apprentissage automatique, diffusion modelségalement connu sous le nom de diffusion probabilistic models ou score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. The goal of diffusion models is to learn a diffusion process that generates a probability distribution for a given dataset from which we can then sample new images. They learn the latent structure of a dataset by modeling the way in which data points diffuse through their latent space.
In the case of vision par ordinateur, diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolutionet image generation. This typically involves training a neural network to sequentially denoise images blurred with Gaussian noise. The model is trained to reverse the process of adding noise to an image. After training to convergence, it can be used for image generation by starting with an image composed of random noise for the network to iteratively denoise. Announced on 13 April 2022, OpenAI's text-to-image model DALL-E 2 is an example that 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. Diffusion models have recently found applications in natural language processing (NLP), particularly in areas like text generation and summarization.
Diffusion models are typically formulated as markov chains and trained using variational inference. Examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.
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