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GMNI:在无监督图形对比学习中实现良好的数据增强.

Xin Xiong1, Xiangyu Wang1, Suorong Yang2

  • 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China; School of Artificial Intelligence, Nanjing University, Nanjing, 210023, China.

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概括

无监督图形对比学习 (GMNI) 引入了通过平衡信息来创建最佳视图的自动化数据增强. 这种新的方法增强了图形表示学习,在各种分类任务中表现优于现有方法.

关键词:
数据增强数据增强图表对比学习学习的图表.图表神经网络的神经网络

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形神经网络 图形神经网络

背景情况:

  • 图形对比学习 (GCL) 是一种强大的无监督方法,用于学习图形表示.
  • 数据增强 (DA) 对GCL至关重要,但最佳的DA策略取决于任务,在无监督环境中很难确定.
  • 现有的GCL方法可能由于DA不足而导致信息不足或冗余,影响性能.

研究的目的:

  • 提出一种新的方法,无监督图形对比学习 (GMNI) 的最小值得注意信息,用于GCL中的自动数据增强.
  • 为应对在无监督GCL中为DA选择任务相关信息的挑战.
  • 通过平衡缺少和过度信息来提高增强视图的质量.

主要方法:

  • GMNI采用对抗性培训策略,以最小值得注意的信息 (MNI) 来产生观点.
  • 最小化优化减少了麻烦信息,同时强调值得注意的信息确保了足够的数据.
  • 基于MNI的随机性被引入以增强视图多样性和模型稳定性.

主要成果:

  • 在无监督和半监督环境中,GMNI在14个数据集中表现出高于现有的GCL方法的卓越性能.
  • 在无监督节点分类中实现了高达1.64%的改进.
  • 在无监督图形分类中实现了高达1.97%的改进,在半监督图形分类中达到3.57%的改进.

结论:

  • 在数据增强中,GMNI有效地平衡了无监督GCL的信息.
  • 拟议的方法显著提高了图形表示学习性能.
  • 在GCL中,GMNI为现有的DA策略提供了一个强大而优秀的替代方案.