基于图形的层-适应-增大对比学习与特征脱相关性
IEEE transactions on pattern analysis and machine intelligence
|October 7, 2025
概括
本研究介绍了基于层适应增强的图形对比学习与特征脱相关性 (LGCLD),以增强图形表示学习. LGCLD提高了模型的稳定性,并减少了功能冗余,以在标签稀缺场景中获得更好的性能.
科学领域:
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
- 人工智能的人工智能
背景情况:
- 图形对比学习 (GCL) 方法对于在标签稀缺的场景中学习表征至关重要.
- 现有的GCL方法在自适应增强和图内特征冗余方面存在局限性.
- 低于最佳的模型稳定性和功能冗余性阻碍了GCL的性能.
研究的目的:
- 提出一个新的图形对比学习框架,LGCLD,解决现有方法的局限性.
- 通过层层的自适应增强来增强模型的强度.
- 通过优化图间协议和图内特征关系来学习信息和非冗余的图表表示.
主要方法:
- 为动态,语义上相似的图形扰动开发了一种层wise适应增强技术.
- 引入了协议-脱相关性 (AD) 损失,以优化图表级别表示协议和特征脱相关性.
- 使用图形信息瓶原理分析了AD损失.
主要成果:
- 通过自适应增强,LGCLD显示了改进的模型稳定性.
- 在图形表示中,AD损失有效地减少了维特征冗余.
- 实验表明,LGCLD在各种图形数据集中实现了竞争性或优异的性能.
结论:
- LGCLD为图形对比学习提供了一种强大而有效的方法.
- 提出的方法解决了增强和表示学习的关键局限性.
- LGCLD推进了对标签稀缺任务的图形表示学习的最先进技术.
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