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Res."],"published-print":{"date-parts":[[2024,10]]},"abstract":"Abstract<\/jats:title>\n The standard approach to tackling computer vision problems is to\n train deep convolutional neural network (CNN) models using\n large-scale image datasets that are representative of the target\n task. However, in many scenarios, it is often challenging to obtain\n sufficient image data for the target task. Data augmentation is a\n way to mitigate this challenge. A common practice is to explicitly\n transform existing images in desired ways to create the required\n volume and variability of training data necessary to achieve good\n generalization performance. In situations where data for the target\n domain are not accessible, a viable workaround is to synthesize\n training data from scratch, i.e., synthetic data augmentation. This\n paper presents an extensive review of synthetic data augmentation\n techniques. It covers data synthesis approaches based on realistic\n 3D graphics modelling, neural style transfer (NST), differential\n neural rendering, and generative modelling using generative\n adversarial networks (GANs) and variational autoencoders (VAEs). For\n each of these classes of methods, we focus on the important data\n generation and augmentation techniques, general scope of application\n and specific use-cases, as well as existing limitations and possible\n workarounds. Additionally, we provide a summary of common synthetic\n datasets for training computer vision models, highlighting the main\n features, application domains and supported tasks. Finally, we\n discuss the effectiveness of synthetic data augmentation methods.\n Since this is the first paper to explore synthetic data augmentation\n methods in great detail, we are hoping to equip readers with the\n necessary background information and in-depth knowledge of existing\n methods and their attendant issues.<\/jats:p>","DOI":"10.1007\/s11633-022-1411-7","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T06:03:37Z","timestamp":1710914617000},"page":"831-869","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["A Survey of Synthetic Data Augmentation Methods\n in Machine Vision"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6103-1925","authenticated-orcid":false,"given":"Alhassan","family":"Mumuni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuseini","family":"Mumuni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nana Kobina","family":"Gerrar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"1411_CR1","unstructured":"A. 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