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A Survey of Synthetic Data Augmentation Methods inMachine Vision

文献类型：期刊论文

作者Alhassan Mumuni1;  Fuseini Mumuni2;  Nana Kobina Gerrar1
刊名Machine Intelligence Research
出版日期2024
卷号21期号:5页码:831-869
关键词Data augmentation generative modelling neural rendering data synthesis synthetic data neural style transfer (NST)
ISSN号2731-538X
DOI10.1007/s11633-022-1411-7
英文摘要The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets that are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly trans form existing images in desired ways to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain are not accessible, a viable workaround is to synthesize training data from scratch, i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modelling, neural style transfer (NST), differential neural rendering, and generative modelling using generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.
源URL[http://ir.ia.ac.cn/handle/173211/59418]  
专题自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位1.Cape Coast Technical University, Cape Coast DL 50, Ghana
2.University of Mines and Technology, Tarkwa 237, Ghana
推荐引用方式
GB/T 7714		 Alhassan Mumuni, Fuseini Mumuni, Nana Kobina Gerrar. A Survey of Synthetic Data Augmentation Methods inMachine Vision[J]. Machine Intelligence Research,2024,21(5):831-869.
APA Alhassan Mumuni, Fuseini Mumuni,& Nana Kobina Gerrar.(2024).A Survey of Synthetic Data Augmentation Methods inMachine Vision.Machine Intelligence Research,21(5),831-869.
MLA Alhassan Mumuni,et al."A Survey of Synthetic Data Augmentation Methods inMachine Vision".Machine Intelligence Research 21.5(2024):831-869.

入库方式： OAI收割

来源：自动化研究所

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