对图像分类的自动数据增强的调查:学习编写,混合和生成
概括
自动化数据增强 (AutoDA) 提供基于学习的方法来改进深度学习模型,超越手动技术. 本调查对AutoDA方法进行了分类,并为其应用提供了实际指导方针.
科学领域:
- 计算机科学,机器学习
- 人工智能的人工智能
背景情况:
- 深度学习模型的概括通常通过数据增强来增强.
- 传统的数据增强依赖于手工操作,需要人类专业知识或广泛的试验.
- 自动数据增强 (AutoDA) 成为一个有前途的研究方向,以学习最佳增强策略.
研究的目的:
- 为了调查和分类最近的自动数据增强 (AutoDA) 方法.
- 分析基于组合,混合和生成的AutoDA方法.
- 讨论挑战,未来前景,并为AutoDA实施提供实际指导方针.
主要方法:
- 将AutoDA方法分为基于组合,混合和生成的方法.
- 对每个类别进行详细分析.
- 讨论应用AutoDA的实际考虑.
主要成果:
- 一个对当前AutoDA技术的结构化概述.
- 在AutoDA中确定关键挑战和未来的研究方向.
- 根据数据集,计算资源和领域知识选择和应用AutoDA的指导方针.
结论:
- 对于手动数据增强来说,AutoDA是一个强大的替代方案,用于改进深度学习模型.
- 本调查为AutoDA领域的研究人员和从业人员提供了全面的参考资料.
- 提供了实用指南,以促进AutoDA方法的有效部署.
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