Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges.

根据目标属性设计和生成新数据一直吸引着分子设计、图像编辑和语音合成等各种关键应用。传统的手工方法严重依赖专业经验和密集的人力，但仍然受到科学知识不足和吞吐量低的困扰，无法支持有效和高效的数据生成。最近，深度学习的进步为表达方法学习数据的底层表示和属性创造了机会。这种功能提供了新的方法来确定数据的结构模式和功能属性之间的相互关系，并在给定所需属性的情况下利用这些关系来生成结构数据。本文是一篇系统综述，解释了这个有前途的研究领域，通常称为可控深度数据生成。首先，本文提出了潜在的挑战并提供了初步准备。然后，本文正式定义了可控深度数据生成，提出了各种技术的分类法，并总结了该特定领域的评估指标。之后，文章介绍了可控深度数据生成的令人兴奋的应用，并对现有工作进行了实验分析和比较。最后，本文强调了可控深度数据生成的有前景的未来方向，并指出了五个潜在的挑战。