DDPM: Denoising Diffusion Probabilistic Models

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DDPM: Denoising Diffusion Probabilistic Models

Diffusion Model for Denoising
- Forward process - add noise
- Reverse process - denoising

Diffusion Model for Denoising

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Paper Title: Denoising Diffusion Probabilistic Models (DDPM)
Paper Source: NIPS, 2020
Paper Address: https://arxiv.org/abs/2006.11239
Paper Code: https://hojonathanho.github.io/diffusion

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for generating models

Includes forward pass (adds Gaussian noise) and reverse pass (generates data)

Both forward and reverse are Markov chains

Forward process - add noise

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According to the Markov chain, the distribution of time xt is only related to time xt-1.
Indicates the continuous multiplication of at, from a1 to at

Reverse process - denoising

The process from noise to clear picture

reasoning from xt to x0

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Use the Bayesian formula to expand:

According to the formula:
, substitute the corresponding values, and use the formula:

to get

where the mean and variance are:
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Origin blog.csdn.net/qq_41701723/article/details/132290954

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