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Article summary:
1. Score-based generative models have achieved impressive empirical results in various applications.
2. Song et al. showed that the training objective of score-based generative models is equivalent to minimizing the Kullback-Leibler divergence of the generated distribution from the data distribution.
3. This work shows that score-based models also minimize the Wasserstein distance between them under suitable assumptions on the model, and provides a novel application of optimal transport theory to prove this result.
Article analysis:
The article is generally trustworthy and reliable, as it provides a clear explanation of its findings and presents evidence to support its claims. The authors provide a detailed proof for their main result, which is based on a novel application of optimal transport theory, and they also provide numerical experiments to support their findings. Furthermore, the article does not appear to be biased or one-sided in any way; it presents both sides equally and does not make any unsupported claims or omit any points of consideration. Additionally, there are no promotional elements or partiality present in the article, and all possible risks are noted throughout. Therefore, overall this article can be considered trustworthy and reliable.