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.
翻译:虽然有许多基于分数的模型,有各种传播战略以及许多分解过程的数字办法,但只有少数著作对基因SDE的得分部分进行了探讨。本文采用新的基因SDE,使用辅助歧视器调整得分。目的是通过估计预先培训的得分估计与真实数据分之间的差距,改进预先培训的传播模型的原始基因化过程。这是通过培训一名将分散的真实数据和分散的抽样数据分类的歧视问题人员来完成的。然后利用差距估计来调整预先培训的得分网络。在实验中,该方法使新的SOTA FID在无条件/有条件的CIFAR-10上实现了1.77.1.64.64,新的SOTA FID/SFID在图像网络256上实现了3.18/4.53。新的SOTA FID/SFID在图像网络256上实现了3.18/4.53。