Denoising Diffusion Probabilistic Models

Generates images by learning to reverse a process that gradually turns data into noise. A network predicts the noise added at each step; sampling starts from pure noise and removes it step by step.

Why it matters

It matched GAN image quality with stable training and launched the diffusion approach behind today's image and video generators.

Read the paper: arXiv:2006.11239

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