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The forward process & ‘schedule' : It was argued [3] that learning an estimate of the true score everywhere on the data space 𝑥 is extremely hard.解决此问题的最受欢迎的解决方案是学习专门用于𝑡的分数。要这样做，需要从𝑝（𝑥）中的样本。The “forward process” is thus designed as an ahead-of-time description of the “path” taken by 𝑝 𝑡 (𝑥) on the probability space (refer to the above figure).One can revert the path by using the same Langevin Equation 4 but with end target being 𝒩(0, 𝐼) and starting from 𝑞 data (𝑥)
从地面建立扩散模型的理论
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