Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and imbalance are common problems in imaging applications that can lead DL models towards biased decision making. A solution to this problem is synthetic data. Synthetic data is an inexpensive substitute to real data for improved accuracy and generalisability of DL models. This survey reviews the recent methods published in relation to the creation and use of synthetic data for computer-vision and medical-imaging DL applications. The focus will be on applications that utilised synthetic data to improve DL models by either incorporating an increased diversity of data that is difficult to obtain in real life, or by reducing a bias caused by class imbalance. Computer-graphics software and generative networks are the most popular data generation techniques encountered in the literature. We highlight their suitability for typical computer-vision and medical-imaging applications, and present promising avenues for research to overcome their computational and theoretical limitations.

深度学习 (DL) 在计算机视觉和医学成像自动化决策应用中表现良好。深度学习的瓶颈源于训练泛化良好的准确模型所需的大量标记数据。数据稀缺和不平衡是成像应用中的常见问题，可能导致深度学习模型做出有偏差的决策。这个问题的解决方案是合成数据。合成数据是真实数据的廉价替代品，可以提高深度学习模型的准确性和通用性。这项调查回顾了最近发布的与计算机视觉和医学成像深度学习应用的合成数据的创建和使用相关的方法。重点将放在利用合成数据来改进深度学习模型的应用程序，方法是合并现实生活中难以获得的增加的数据多样性，或减少由类别不平衡引起的偏差。计算机图形软件和生成网络是文献中最流行的数据生成技术。我们强调了它们对典型计算机视觉和医学成像应用的适用性，并提出了克服其计算和理论限制的有希望的研究途径。