About: Diffusion model

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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 were introduced in 2015 with a motivation from non-equilibrium thermodynamics.

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rdfs:label	Diffusion model (en)
rdfs:comment	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 were introduced in 2015 with a motivation from non-equilibrium thermodynamics. (en)
dcterms:subject	Artificial intelligence Machine learning Machine learning algorithms Markov models
Wikipage page ID	71912239 (xsd:integer)
Wikipage revision ID	1122479754 (xsd:integer)
Link from a Wikipage to another Wikipage	Inpainting Artificial intelligence Gaussian noise Machine learning Computer vision Text-to-image model Machine learning Machine learning algorithms Non-equilibrium thermodynamics Diffusion process Markov models Latent variable model Super-resolution Image denoising DALL-E 2 OpenAI Markov chain Variational Bayesian methods Variational autoencoder Variational inference Denoise
sameAs	Diffusion model Diffusion model Diffusion model
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:Short_description dbt:Artificial-intelligence-stub
has abstract	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 were introduced in 2015 with a motivation from non-equilibrium thermodynamics. 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. Announced on 13 April 2022, OpenAI's text-to-image model DALL-E 2 is a recent example. It 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. (en)
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foaf:isPrimaryTopicOf	wikipedia-en:Diffusion_model
is Link from a Wikipage to another Wikipage of	DALL-E Generative model Text-to-image model DreamBooth NovelAI Stable Diffusion Diffusion models Diffusion probabilistic model Diffusion probabilistic models
is Wikipage redirect of	Diffusion models Diffusion probabilistic model Diffusion probabilistic models
is foaf:primaryTopic of	wikipedia-en:Diffusion_model

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