This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of privacy-sensitive data. Additionally, we highlight the advantages of using deep generative models over other methods and provide a detailed explanation of the underlying concepts, including unsupervised learning, neural networks, and generative models. The paper covers the challenges and considerations involved in using deep generative models for tabular datasets, such as data normalization, privacy concerns, and model evaluation. This review provides a valuable resource for researchers and practitioners interested in synthetic data generation and its applications.

翻译：本文对近年来通过深度生成模型进行合成数据生成的最新进展进行了全面综合，重点聚焦于表格数据集。我们特别阐述了在隐私敏感数据背景下合成数据生成的重要性。此外，我们强调了使用深度生成模型相较于其他方法的优势，并详细解释了其底层概念，包括无监督学习、神经网络和生成模型。本文涵盖了将深度生成模型应用于表格数据集时所涉及的挑战与考量，例如数据归一化、隐私问题和模型评估。本综述为对合成数据生成及其应用感兴趣的研究人员和从业者提供了宝贵的参考资料。