自动SGRL:自动监督图形表示学习的自动化框架构建
1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China.
概括
本研究介绍了AutoSGRL,这是一种用于自动构建自我监督图形表示学习框架的新方法. AutoSGRL使用遗传算法优化图形对比学习,优于手工设计.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 自动机器学习 (AutoML) 正在推动人工智能发展.
- 目前的图形神经架构搜索主要集中在监督或半监督学习上.
- 在自主监督图形表示学习的自动化方法中存在差距.
研究的目的:
- 提出AutoSGRL,这是自动构建灵活自主监督图形表示学习框架的第一个方法.
- 为自主监督图形表示学习建立一个全面的搜索空间,包括数据增强,代理任务和超参数.
- 开发一个自动化的搜索引擎来优化这些框架.
主要方法:
- 开发了AutoSGRL,这是一个利用现有的自我监督图形对比学习方法的框架.
- 定义了一个包含数据增强,代理任务和图形对比学习的超参数的搜索空间.
- 实现了一个基于遗传算法的搜索引擎,模拟生物进化 (选择,交叉,突变) 以代优化框架.
主要成果:
- AutoSGRL成功构建了自我监督的图形表示学习框架.
- 拟议的方法实现了与最先进的手工方法相比或更好的性能.
- 对手动自我监督方法和半监督图形神经架构搜索都证明了有效性.
结论:
- 自动SGRL代表了自动图表表示学习的重大进步.
- 该方法提供了一种灵活和有效的方法来构建自主监督的图形学习框架.
- AutoSGRL为更容易获得和更强大的图形表示学习解决方案铺平了道路.
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