提出新方法解决图生成中去噪误差累积问题，效果显著优于现有模型。
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
- 假设中间噪声状态条件独立，简化去噪流程
- 引入判别器选择性保留或破坏节点，提升生成质量
- 在多个图生成任务上表现更优，适合结构生成研究者
离散扩散与流匹配模型在离散结构生成（如图）方面取得显著进展。然而，中间噪声状态间的依赖关系导致反向去噪过程中误差累积与传播，即复合去噪误差问题。为此，我们提出一种新框架——简单迭代去噪，通过假设中间状态条件独立来简化离散扩散过程，从而规避该问题。此外，我们引入一个判别器（Critic），在生成时根据数据分布下元素的似然性，选择性地保留或破坏实例中的成分。实证评估表明，该方法在图生成任务中显著优于现有离散扩散基线。
原文摘要 · Abstract (English)
Discrete Diffusion and Flow Matching models have significantly advanced generative modeling for discrete structures, including graphs. However, the dependencies between intermediate noisy states lead to error accumulation and propagation during the reverse denoising process - a phenomenon known as compounding denoising errors. To address this problem, we propose a novel framework called Simple Iterative Denoising, which simplifies discrete diffusion and circumvents the issue by assuming conditional independence between intermediate states. Additionally, we enhance our model by incorporating a Critic. During generation, the Critic selectively retains or corrupts elements in an instance based on their likelihood under the data distribution. Our empirical evaluations demonstrate that the proposed method significantly outperforms existing discrete diffusion baselines in graph generation tasks.
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