Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.

翻译：深度学习算法在各类计算机视觉任务中展现出卓越性能。然而，有限的标注数据会导致网络过拟合问题，使得网络对未见数据的表现劣于训练数据，进而限制了性能提升。为解决此问题，研究人员提出了多种技术，包括Dropout、归一化及先进数据增强方法。其中，数据增强通过引入样本多样性扩大数据集规模，已成为近年来的研究热点。本文聚焦于先进数据增强技术，系统阐述其研究背景，提出新颖且全面的分类体系，分析各类技术的优势与局限（基于可行性），并综合评估数据增强对图像分类、目标检测和语义分割三大主流视觉任务的影响。为确保结果可复现性，我们已整理所有数据增强技术的开源代码。最后，探讨当前挑战与难点，并展望未来研究方向。本综述旨在为研究者提供多重价值：i）阐明数据增强缓解过拟合问题的机制原理；ii）对比实验结果可节省研究者检索时间；iii）文中所述技术的代码已开源于https://github.com/kmr2017/Advanced-Data-augmentation-codes；iv）未来工作将激发学界研究兴趣。