Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or learning strategies, we adopt a holistic perspective that emphasizes the universality and robustness of representation learning methods across diverse downstream tasks. We examine recent advances in data augmentation and generation, specialized neural network architectures tailored to tabular data, and innovative learning objectives that enhance representation quality. Additionally, we highlight the growing influence of self-supervised learning and the adaptation of transformer-based foundation models for tabular data. Our review is based on a systematic literature search using rigorous inclusion criteria, encompassing 127 papers published since 2020 in top-tier conferences and journals. Through detailed analysis and comparison, we identify emerging trends, critical gaps, and promising directions for future research, aiming to guide the development of more generalizable and effective tabular data representation methods.

本研究针对表格数据表示学习中的独特挑战进行全面评述，包括数据不规则性、特征分布异质性及复杂的列间依赖关系。文章提出了一种整体视角，强调表格数据表示学习方法的普适性与稳健性，并指出自监督学习和基于变换器的基础模型对表格数据的影响。通过分析127篇相关文献，识别出未来研究的趋势和关键空白，有助于推动更具普适性和有效性的表格数据表示方法的发展。

表格数据中的深度学习：基础、挑战、进展与未来方向