While there are many score-based models with various diffusing strategies as
well as many numerical schemes of the denoising process, only a few works have
explored the score part of the generative SDE. This paper introduces a new
generative SDE with score adjustment using an auxiliary discriminator. The goal
is to improve the original generative process of a pre-trained diffusion model
by estimating the gap between the pre-trained score estimation and the true
data score. This is done by training a discriminator that classifies diffused
real data and diffused sample data. The gap estimation is then used to adjust
the pre-trained score network. In experiments, the method enables new SOTA FIDs
of 1.77/1.64 on unconditional/conditional CIFAR-10, and new SOTA FID/sFID of
3.18/4.53 on ImageNet 256x256.

本研究引入了一个新的生成 SDE 模型，采用辅助鉴别器进行得分调整，通过训练鉴别器来估计预训练得分估计和真实数据得分之间的差距，进而进行调整。实验结果显示，该方法在无条件 / 有条件的 CIFAR-10 上实现了新的 SOTA FIDs 1.77/1.64，以及 ImageNet 256x256 上的新的 SOTA FID /sFID 3.18 / 4.53。