Image synthesis designed for machine learning applications provides the means to efficiently generate large quantities of training data while controlling the generation process to provide the best distribution and content variety. With the demands of deep learning applications, synthetic data have the potential of becoming a vital component in the training pipeline. Over the last decade, a wide variety of training data generation methods has been demonstrated. The potential of future development calls to bring these together for comparison and categorization. This survey provides a comprehensive list of the existing image synthesis methods for visual machine learning. These are categorized in the context of image generation, using a taxonomy based on modelling and rendering, while a classification is also made concerning the computer vision applications they are used. We focus on the computer graphics aspects of the methods, to promote future image generation for machine learning. Finally, each method is assessed in terms of quality and reported performance, providing a hint on its expected learning potential. The report serves as a comprehensive reference, targeting both groups of the applications and data development sides. A list of all methods and papers reviewed herein can be found at https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/.

中文翻译：

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视觉机器学习图像合成方法综述

为机器学习应用程序设计的图像合成提供了有效生成大量训练数据的方法，同时控制生成过程以提供最佳分布和内容多样性。随着深度学习应用的需求，合成数据有可能成为训练管道中的重要组成部分。在过去的十年中，已经证明了各种各样的训练数据生成方法。未来发展的潜力要求将这些组合在一起进行比较和分类。该调查提供了用于视觉机器学习的现有图像合成方法的完整列表。这些在图像生成的上下文中进行分类，使用基于建模和渲染的分类法，同时也对它们所使用的计算机视觉应用进行了分类。我们专注于方法的计算机图形方面，以促进机器学习的未来图像生成。最后，根据质量和报告的性能对每种方法进行评估，提供有关其预期学习潜力的提示。该报告作为综合参考，针对应用和数据开发方的两个群体。可以在 https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/ 找到此处审查的所有方法和论文的列表。提供有关其预期学习潜力的提示。该报告作为综合参考，针对应用和数据开发方的两个群体。可以在 https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/ 找到此处审查的所有方法和论文的列表。提供有关其预期学习潜力的提示。该报告作为综合参考，针对应用和数据开发方的两个群体。可以在 https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/ 找到此处审查的所有方法和论文的列表。