Stochastic Differential Equations (SDE) in score-based generative model solve conditioned inverse problems such as inpainting, colorization

Score-based generative models show good performance recently in image generation. In the context of statistics, Score is defined as the gradient of logarithmic probability density with respect to the data distribution parameter. Usually, while training a generative model, noises are added to the original image, and the model learns to revert the noisy image back to its original form. In a score-based generative model, noises are added in steps such that the final noisy image follows a predefined probability distribution. A trained model generates the original image from the predefined distribution following the score estimated at each step during noising.

Read more: https://analyticsindiamag.com/score-based-sde-in-generative-modeling/

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