Data plays a crucial role in machine learning. However, in real-world applications, there are several problems with data, e.g., data are of low quality; a limited number of data points lead to under-fitting of the machine learning model; it is hard to access the data due to privacy, safety and regulatory concerns. \textit{Synthetic data generation} offers a promising new avenue, as it can be shared and used in ways that real-world data cannot. This paper systematically reviews the existing works that leverage machine learning models for synthetic data generation. Specifically, we discuss the synthetic data generation works from several perspectives: (i) applications, including computer vision, speech, natural language, healthcare, and business; (ii) machine learning methods, particularly neural network architectures and deep generative models; (iii) privacy and fairness issue. In addition, we identify the challenges and opportunities in this emerging field and suggest future research directions.

翻译：数据在机器学习中扮演着至关重要的角色。然而，在实际应用中，数据存在若干问题，例如数据质量低下、数据点数量有限导致机器学习模型欠拟合，以及因隐私、安全和监管问题而难以获取数据。合成数据生成提供了一种有前景的新途径，因为它能以真实数据无法实现的方式被共享和使用。本文系统性地综述了现有利用机器学习模型进行合成数据生成的研究工作。具体而言，我们从多个视角讨论了合成数据生成工作：（i）应用领域，包括计算机视觉、语音、自然语言、医疗保健和商业；（ii）机器学习方法，特别是神经网络架构和深度生成模型；（iii）隐私和公平性问题。此外，我们识别了这一新兴领域的挑战与机遇，并提出了未来研究方向。