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Eric Zelikman 2020-12-10 12:51:43 -05:00
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C2W2_(Optional_Notebook)_Score_Based_Generative_Modeling.ipynb
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"where $\\bar{\\mathbf{w}}$ is a Brownian motion in the reverse time direction, and $dt$ here represents an infinitesimal negative time step. Here $p_t(\\mathbf{x})$ represents the distribution of $\\mathbf{x}(t)$. This reverse SDE can be computed once we know the drift and diffusion coefficients of the forward SDE, as well as the score of $p_t(\\mathbf{x})$ for each $t\\in[0, T]$.\n",
"\n",
"The overall intuition of score-based generative modeling with SDEs can be summarized in the illustration below\n",
"![sde schematic](https://drive.google.com/uc?id=1cHHEE3Cfo82Oiir-fLlE1rmSmD5l1ay5)\n",
"![sde schematic](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/diffusion_schematic.jpg?raw=true)\n",
"\n",
"### Score Estimation\n",
"\n",