Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution. Then, the algorithms are split into three categories; model-free, model-based, and optimizing policy-based. The model-free category employs image processing methods while the model-based method leverages trainable image generation models. In contrast, the optimizing policy-based approach aims to find the optimal operations or their combinations. Furthermore, we discuss the current trend of common applications with two more active topics, leveraging different ways to understand image augmentation, such as group and kernel theory, and deploying image augmentation for unsupervised learning. Based on the analysis, we believe that our survey gives a better understanding helpful to choose suitable methods or design novel algorithms for practical applications.
翻译:深层学习在需要大量图像的计算机愿景中取得了体面的绩效,然而,在很多情况下,收集图像是昂贵和困难的。为了缓解这一问题,许多图像增强算法被提议为有效和高效的战略。理解当前的算法对于寻找合适的方法或开发适合特定任务的新技术至关重要。在本文中,我们用新的信息分类法对用于深层学习的图像增强进行全面调查。为了获得我们需要图像增强的基本理念,我们引入了计算机愿景任务和周边分布方面的挑战。然后,这些算法被分为三类:无模型、基于模型和优化政策。无模型的类别使用图像处理方法,而模型方法则利用可培训的图像生成模型模型。相比之下,优化政策方法旨在找到最佳操作或其组合。此外,我们用两个更活跃的专题来讨论共同应用当前的趋势,利用不同的方法来理解图像增强,例如组合和内核理论,以及将图像增强用于非超超能力学习。基于分析,我们认为,我们的调查为选择适合的实用方法或设计新式应用提供了更好的理解。