Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. Additionally, it explores different machine learning methods, with particular emphasis on neural network architectures and deep generative models. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation. Furthermore, this study identifies the challenges and opportunities prevalent in this emerging field, shedding light on the potential avenues for future research. By delving into the intricacies of synthetic data generation, this paper aims to contribute to the advancement of knowledge and inspire further exploration in synthetic data generation.

翻译：机器学习高度依赖数据，但在实际应用中常面临多种数据相关问题，包括数据质量低下、数据量不足导致机器学习模型欠拟合，以及因隐私、安全和监管顾虑造成的数据获取困难。面对这些挑战，合成数据生成概念应运而生，它作为一种有前景的替代方案，能够以真实数据无法实现的方式促进数据共享与利用。本文对现有采用机器学习模型进行合成数据生成的研究开展了全面系统综述。该综述从多维度展开：首先梳理合成数据生成的应用场景，涵盖计算机视觉、语音、自然语言处理、医疗及商业领域；其次探讨不同机器学习方法，重点聚焦神经网络架构与深度生成模型；同时论述与合成数据生成相关的隐私与公平性关键问题。此外，本研究识别出该新兴领域面临的挑战与机遇，揭示了未来研究的潜在方向。通过深入剖析合成数据生成的复杂性，本文旨在推动该领域知识进步，并激发对合成数据生成的进一步探索。