提出新型生成模型，高效生成无序点集数据。
Generative Unordered Flow for Set-Structured Data Generation
- 将无序数据转为函数表示，用流匹配学习其概率分布。
- 通过模拟粒子过滤与梯度搜索，实现函数到点集的逆映射。
- 在多个真实数据集上超越现有方法，适合点云生成任务。
基于流的生成模型在图像、文本等多种数据模态中表现优异，但其在无序数据（如空间点集）上的应用仍较少，因以往模型多针对有序向量数据设计。本文提出无序流（Unordered Flow），一种用于集合结构数据生成的流基生成模型。具体地，将无序数据转换为适当的函数表示，并通过函数值流匹配学习其概率测度。对于从函数表示到无序数据的逆映射，提出类似粒子滤波的方法：先用Langevin动力学预热初始粒子，再通过梯度搜索迭代更新直至收敛。在多个真实世界数据集上进行了广泛实验，结果表明该模型在生成集合结构数据方面非常有效，显著优于现有基线方法。
原文摘要 · Abstract (English)
Flow-based generative models have demonstrated promising performance across a broad spectrum of data modalities (e.g., image and text). However, there are few works exploring their extension to unordered data (e.g., spatial point set), which is not trivial because previous models are mostly designed for vector data that are naturally ordered. In this paper, we present unordered flow, a type of flow-based generative model for set-structured data generation. Specifically, we convert unordered data into an appropriate function representation, and learn the probability measure of such representations through function-valued flow matching. For the inverse map from a function representation to unordered data, we propose a method similar to particle filtering, with Langevin dynamics to first warm-up the initial particles and gradient-based search to update them until convergence. We have conducted extensive experiments on multiple real-world datasets, showing that our unordered flow model is very effective in generating set-structured data and significantly outperforms previous baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。