在图表上进行分布外通用化:一项调查
概括
本文调查了图形机器学习的分布外 (OOD) 泛化,解决了数据分布变化时的性能下降. 它对方法进行了分类,并讨论了强大的图形模型的未来研究方向.
科学领域:
- 图表机器学习 图表机器学习
- 人工智能的人工智能是人工智能.
- 数据科学是数据科学.
背景情况:
- 大多数图形机器学习依赖于分布式假设,假设训练和测试数据分布相同.
- 现实世界的图形数据通常会出现分布变化,导致现有模型的性能显著下降.
- 对图形的分布外 (OOD) 概括对于在多样化,动态环境中强大的性能至关重要.
研究的目的:
- 提供对图形机器学习的分发外 (OOD) 概括技术的全面调查.
- 在图形数据的背景下正式定义OOD泛化问题.
- 审查和分类最近在OOD概括图的进步.
主要方法:
- 现有的OOD概括方法分为三类:以数据为中心,以模型为中心和以学习策略为中心.
- 详细讨论每个类别中的方法,根据它们的整合到图形机器学习管道.
- 对理论基础和常用的基准数据集的审查,用于评估OOD图表概括.
主要成果:
- 在图形机器学习中对OOD概括的当前情景进行结构化概述.
- 确定现有方法中的关键挑战和局限性.
- 综合理论基础和经验评估策略.
结论:
- OOD泛化是推进图形机器学习的关键领域,超出了分布式假设.
- 一个系统的分类为理解和开发新的OOD图表方法提供了一个框架.
- 未来的研究应该专注于解决已识别的挑战,以提高图形模型在现实场景中的稳定性和适用性.
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