用离散扩散模型生成带约束的图路径，保证结果必为有效路径。
DDPS: Discrete Diffusion Posterior Sampling for Paths in Layered Graphs
- 用填充邻接表矩阵表示路径，显式编码路径结构。
- 无需重训练即可通过分类器引导生成特定目标边路径。
- 在满足路径约束方面优于不考虑约束的基线方法。
扩散模型是当前生成模型的重要类别，在前沿人工智能研究中占据主导地位。尽管已有众多图像、视频生成之外的拓展，但很少有方法能显式处理生成样本中的约束问题。本文研究在分层图（一种有向无环图变体）中使用离散扩散模型生成路径，并确保生成结果确实是有效路径。提出一种简单有效的路径表示方法——填充邻接表矩阵（PALM）。此外，展示了如何有效实现分类器引导，使采样路径可被引导至特定偏好边，且无需重新训练扩散模型。初步实验表明，本方法在满足路径约束方面显著优于不显式考虑路径约束的替代方案。
原文摘要 · Abstract (English)
Diffusion models form an important class of generative models today, accounting for much of the state of the art in cutting edge AI research. While numerous extensions beyond image and video generation exist, few of such approaches address the issue of explicit constraints in the samples generated. In this paper, we study the problem of generating paths in a layered graph (a variant of a directed acyclic graph) using discrete diffusion models, while guaranteeing that our generated samples are indeed paths. Our approach utilizes a simple yet effective representation for paths which we call the padded adjacency-list matrix (PALM). In addition, we show how to effectively perform classifier guidance, which helps steer the sampled paths to specific preferred edges without any retraining of the diffusion model. Our preliminary results show that empirically, our method outperforms alternatives which do not explicitly account for path constraints.
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