增强图形神经网络,用于分布之外的图形检测
概括
本研究介绍了ODGNN,这是一种用于分布外 (OOD) 图形检测的新算法,可以在没有OOD数据的情况下训练图形神经网络 (GNN),从而提高现实应用的检测精度.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 计算机视觉 计算机视觉
背景情况:
- 图形神经网络 (GNN) 在图形分类方面表现出色,但在分布之外 (OOD) 检测方面却很难.
- 现有的OOD检测方法经常使用合成OOD数据,这可能不涵盖现实世界OOD图的全部范围.
- 这种限制阻碍了在开放世界的场景中可靠地部署GNN.
研究的目的:
- 开发一种新的算法,用于强大的OOD图形检测.
- 为了提高GNN在识别不熟悉的图形分布的性能,而没有先前接触过OOD样本.
- 在现实,开放世界的环境中提高GNN的可靠性.
主要方法:
- 提出了ODGNN,这是一种算法,可以在没有任何OOD数据的情况下训练GNN,从而减轻偏差.
- 在GNN中集成高斯编码器,在训练期间学习在分布 (ID) 中的类条件分布.
- 推断通过它们在表示空间中与已学习的ID分布的分歧来识别OOD图.
主要成果:
- 在各种GNN模型中,ODGNN有效地提高了OOD检测性能.
- 与最先进的OOD图形检测方法相比,显示出更高的性能.
- 通过对现实世界的数据集进行广泛的实验来验证.
结论:
- 通过避免OOD训练数据,ODGNN提供了一种新的OOD图形检测方法.
- 该方法提高了GNN在分布和分布之外的图形之间区分的能力.
- ODGNN增强了GNN在不同的,不可预测的环境中的实际应用性.
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