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Score-based diffusion models corrupt data by gradually adding Gaussian noise and then learn to reverse this process by estimating the score, the gradient of the log-density.
Diffusion models learn to reverse a simple noising process by estimating the score (the gradient of the log density) of data at different noise levels.