Data augmentation is a crucial technique for expanding training datasets, effectively alleviating the overfitting issue that arises from limited training data in deep learning models. This paper takes a fresh perspective and offers a scholarly exploration of image data augmentation, following a logical progression from unlearnable to learnable methods. The paper begins by providing a brief overview of the developmental history of data augmentation. It categorizes data augmentation techniques into unlearnable and learnable based on their “variation” strategies. Furthermore, the paper outlines the fundamental properties of data augmentation, including expansiveness, fidelity, generalizability, and self-adaptability. Subsequently, focusing on unlearnable and learnable data augmentation techniques, the paper further divides them into single-sample and multi-sample, global and local, image domain, and feature domain, categorically reviewing the basic principles and effects of various data augmentation methods based on the differences in the sources, scopes, and content of “variation” attributes. Ultimately, the comparative analysis of diverse data augmentation methodologies in specific tasks is conducted alongside a synthesis and projection of future research directions. By comprehensively analyzing diverse image data augmentation methods from a fresh perspective, this review reveals the intrinsic disparities between unlearnable and learnable data augmentation techniques. It paves the way for scholars to embark on innovative paths in data augmentation.

中文翻译：

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图像数据增强的前沿和发展：从不可学习到可学习


数据增强是扩展训练数据集的关键技术，可以有效缓解深度学习模型中有限训练数据带来的过拟合问题。本文采用了全新的视角，并对图像数据增强进行了学术探索，遵循从不可学习到可学习方法的逻辑进展。本文首先简要概述了数据增强的发展历史。它根据数据增强技术的“变异”策略将数据增强技术分为不可学习的和可学习的。此外，本文还概述了数据增强的基本属性，包括可扩展性、保真度、泛化性和自适应性。随后，论文围绕不可学习和可学习的数据增强技术，进一步将其分为单样本和多样本、全局和局部、图像域和特征域，分类回顾了基于数据增强的各种数据增强方法的基本原理和效果。 “变异”属性的来源、范围和内容的差异。最终，对特定任务中的不同数据增强方法进行比较分析，同时对未来研究方向进行综合和预测。通过从全新的角度全面分析不同的图像数据增强方法，这篇综述揭示了不可学习和可学习的数据增强技术之间的内在差异。它为学者们走上数据增强的创新道路铺平了道路。