IBPL:基于信息瓶的快速学习,用于图形分布外检测
Yanan Cao1, Fengzhao Shi1, Qing Yu2
1Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China.
概括
本研究介绍了基于信息瓶的快速学习 (IBPL),用于稳健的图形分布外 (OOD) 检测. 通过减少重叠特征,IBPL有效地区分分布式 (ID) 和OOD图形,提高图形学习系统的可靠性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 在训练和测试数据分布不同时,图形分布外 (OOD) 检测对于可靠的图形学习系统至关重要.
- 当前基于提示的图形方法是高效的,但忽视重叠的特征,阻碍了OOD检测性能.
研究的目的:
- 解决现有的图表提示方法在处理分布内 (ID) 和OOD图表之间的重叠特征方面的局限性.
- 通过开发一种新的快速学习方法,提高图形OOD检测的准确性和稳定性.
主要方法:
- 拟议的基于信息瓶的快速学习 (IBPL) 具有新的图表提示掩盖节点特征和图形结构.
- 利用信息瓶 (IB) 目标与噪音数据增强来消除重叠的特征.
- 在提示图和类别标签之间最大化相互信息以提取ID特征,减轻扰乱图的负面影响.
主要成果:
- IBPL在多个现实数据集的图形OOD检测中表现出卓越的性能.
- 该方法在监督和无监督学习场景中都被证明有效.
- 经验结果和分析证实了IBPL在竞争基线上的有效性.
结论:
- 通过有效处理重叠特征,IBPL在图形OOD检测方面取得了重大进展.
- 拟议的方法提高了图形学习系统的可靠性和安全性.
- IBPL提供了一个计算高效和有效的解决方案来检测OOD图.
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