达利布:一个库存的库存库,用于计算机视觉中的数据增强
Sofia Amarù1, Davide Marelli1, Gianluigi Ciocca1
1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
Journal of imaging
|October 27, 2023
概括
数据增强是通过扩展数据集来增强机器学习模型的关键. 这项研究调查了流行的计算机视觉数据增强库,为从业者提供了一份指南.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 数据增强对于通过增加训练数据集大小来提高机器学习模型的概括性和稳定性至关重要.
- 现在,各种图书馆在不同的机器学习任务中简化了各种数据增强策略的实施.
研究的目的:
- 调查广泛采用的数据增强库,专门用于计算机视觉任务.
- 为从业者提供一个全面的指南,以便有效地导航和利用这些资源.
主要方法:
- 一个精心策划的分类系统被开发出来,用于分类图书馆使用的不同数据增强方法.
- 应用示例伴随着分类,以说明实际使用.
- 创建了一个公共网站DALib,作为分类学,方法和示例的集中存储库.
主要成果:
- 该调查确定并对计算机视觉可用的关键数据增强库进行了分类.
- 开发的分类学提供了对增强技术的结构化概述.
- DALib网站为探索这些资源提供了一个易于访问的平台.
结论:
- 对计算机视觉项目的数据增强技术的知情选择是通过这项调查和资源来促进的.
- 这种全面的资源旨在通过有效的数据增强来赋予从业者权力,并推进计算机视觉研究.
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