Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data, and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, we propose a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities. Specifically, from a data-centric perspective, this survey proposes a modality-independent taxonomy by investigating how to take advantage of the intrinsic relationship between data samples, including single-wise, pair-wise, and population-wise sample data augmentation methods. Additionally, we categorize data augmentation methods across five data modalities through a unified inductive approach.

翻译：数据增强是一系列通过对现有数据样本进行操作以生成高质量人工数据的技术。借助数据增强技术，人工智能模型能够在涉及稀缺或不平衡数据集的任务中显著提升适用性，从而大幅增强模型的泛化能力。现有文献综述仅关注特定类型的模态数据，并从模态特定和操作导向的角度对这些方法进行分类，缺乏对跨模态数据增强方法的一致性总结，限制了对现有数据样本如何服务于数据增强过程的理解。为填补这一空白，我们提出一种更具启发性的分类体系，涵盖针对不同常见数据模态的数据增强技术。具体而言，本综述从数据中心的视角出发，通过探究如何利用数据样本间的内在关系（包括单样本、成对样本和群体样本的数据增强方法），提出一种与模态无关的分类体系。此外，我们通过统一的归纳方法，对五种数据模态的数据增强方法进行了分类。