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。

基于得分扩散模型的生成过程优化与鉴别器指导

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Automated essay scoring plays an important role in judging students' language
abilities in education. Traditional approaches use handcrafted features to
score and are time-consuming and complicated. Recently, neural network
approaches have improved performance without any feature engineering. Unlike
other natural language processing tasks, only a small number of datasets are
publicly available for automated essay scoring, and the size of the dataset is
not sufficiently large. Considering that the performance of a neural network is
closely related to the size of the dataset, the lack of data limits the
performance improvement of the automated essay scoring model. In this paper, we
proposed a method to increase the number of essay-score pairs using
back-translation and score adjustment and applied it to the Automated Student
Assessment Prize dataset for augmentation. We evaluated the effectiveness of
the augmented data using models from prior work. In addition, performance was
evaluated in a model using long short-term memory, which is widely used for
automated essay scoring. The performance of the models was improved by using
augmented data to train the models.

本文提出了一种使用回译和分数调整来增加论文 - 评分对数目的方法，并将其应用于 Automated Student Assessment Prize 数据集进行扩充，通过使用先前工作中的模型对增强数据的有效性进行了评估，并使用长短期记忆进行了性能评估，该模型广泛用于自动文章评分。使用增强数据来训练模型可以提高模型的性能。