改进扩散模型中的判别器引导，提升生成样本质量
Improving Discriminator Guidance in Diffusion Models
- 提出新的判别器训练目标，更有效地最小化分布差距
- 实验证明新方法在多个数据集上生成样本质量更高
- 适合关注扩散模型生成效果优化的研究者
判别器引导已成为高效微调预训练得分匹配扩散模型的常用方法。然而，本文表明，该技术的标准实现并不一定使模型分布更接近真实数据分布。具体而言，我们发现使用交叉熵损失训练判别器（如常规做法）可能反而增加模型与目标分布之间的KL散度，尤其是在判别器过拟合时。为解决此问题，我们提出了一个理论上合理的判别器引导训练目标，能够正确最小化KL散度。我们分析了其性质，并在多个数据集上进行了实证，结果表明所提方法在生成样本质量上持续优于传统方法。
原文摘要 · Abstract (English)
Discriminator Guidance has become a popular method for efficiently refining pre-trained Score-Matching Diffusion models. However, in this paper, we demonstrate that the standard implementation of this technique does not necessarily lead to a distribution closer to the real data distribution. Specifically, we show that training the discriminator using Cross-Entropy loss, as commonly done, can in fact increase the Kullback-Leibler divergence between the model and target distributions, particularly when the discriminator overfits. To address this, we propose a theoretically sound training objective for discriminator guidance that properly minimizes the KL divergence. We analyze its properties and demonstrate empirically across multiple datasets that our proposed method consistently improves over the conventional method by producing samples of higher quality.
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