Core principles of Denoising Diffusion Models and Score Matching explained
ariG23498 · x · 2026-09-01
The post provides concise explanations of two core generative modeling concepts:
- Denoising Diffusion Models: The process involves adding Gaussian noise to clean data step-by-step until it becomes random, after which the model learns the reverse denoising trajectory to restore the image.
- Score Matching: It involves calculating the gradient in a probability density space and moving in that direction, analogous to walking with a compass towards the actual data distribution.
Related event: Explaining Denoising Diffusion Models and Score Matching(2 posts)→
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