In recent years, generative diffusion models have achieved a rapid paradigm shift in deep generative models by showing groundbreaking performance across various applications. Meanwhile, structured data, encompassing tabular and time series data, has been received comparatively limited attention from the deep learning research community, despite its omnipresence and extensive applications. Thus, there is still a lack of literature and its review on structured data modelling via diffusion models, compared to other data modalities such as computer vision and natural language processing. Hence, in this paper, we present a comprehensive review of recently proposed diffusion models in the field of structured data. First, this survey provides a concise overview of the score-based diffusion model theory, subsequently proceeding to the technical descriptions of the majority of pioneering works using structured data in both data-driven general tasks and domain-specific applications. Thereafter, we analyse and discuss the limitations and challenges shown in existing works and suggest potential research directions. We hope this review serves as a catalyst for the research community, promoting the developments in generative diffusion models for structured data.

翻译：近年来，生成扩散模型在深度生成模型中实现了快速范式转变，并在各种应用中展现出突破性性能。与此同时，结构化数据（包括表格数据和时间序列数据）尽管无处不在且应用广泛，却相对较少受到深度学习研究界的关注。因此，与其他数据模态（如计算机视觉和自然语言处理）相比，目前仍缺乏关于通过扩散模型对结构化数据进行建模的文献及其综述。为此，本文全面回顾了近期在结构化数据领域提出的扩散模型。首先，本综述简要概述了基于得分的扩散模型理论，随后对大多数在数据驱动通用任务和特定领域应用中使用结构化数据的开创性工作进行了技术描述。接着，我们分析讨论了现有工作中的局限性与挑战，并提出了潜在研究方向。我们希望本综述能作为研究界的催化剂，推动结构化数据生成扩散模型的发展。