通过必要性和充分性的概率统一图形分布外的不变和变异特征
Xuexin Chen1, Ruichu Cai2, Kaitao Zheng1
1School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
概括
本研究引入了一种新的方法,通过使用必要性和充分性概率 (PNS) 提取足够和必要的不变子结构来实现图形分布外 (OOD) 概括. 拟议的SNIGL模型通过结合不变和域变异子图分类器来增强概括性.
科学领域:
- 图表 机器学习 机器学习
- 在分布之外的泛化.
背景情况:
- 图形分布外 (OOD) 泛化对于现实应用至关重要,它使得在有偏见的数据上训练的模型能够在未见的数据上执行.
- 目前的方法通常依赖于提取不变子图,但可能会遭受语义子图丢失或冗余,导致次优概括.
- 环境增强是一种常见的技术,但其在保存基本图形结构方面的有效性有限.
研究的目的:
- 通过识别足够的和必要的不变子结构,开发一种用于强大的图形OOD概括的新方法.
- 解决现有方法在保存语义信息和改进概括方面的局限性.
- 为子图提取提出基于必要性和充分性的概率 (PNS) 的理论框架.
主要方法:
- 利用必要性和充分性的概率 (PNS) 来从图形数据中提取足够和必要的不变子结构.
- 开发了充足性和必要性启发图形学习 (SNIGL) 模型,该模型结合了不变和域变量子图形分类器.
- 利用与标签相关的域变量子图来以合集的方式增强概括性能.
主要成果:
- 与最先进的技术相比,SNIGL模型在六个公共基准中表现出优越的性能.
- 该方法有效地提取了足够和必要的不变子结构,提高了概括能力.
- 实验结果验证了对图形OOD概括提出的整体方法的有效性.
结论:
- 基于PNS的拟议SNIGL模型在图形OOD泛化方面取得了重大进展.
- 组合策略结合了不变和域变异子图分类器,在促进概括方面被证明是有效的.
- 这些发现突显了SNIGL在需要强大的图形数据分析的现实应用中的潜力.
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