TO-UGDA:以目标为导向的无监督图域调整
Zhuo Zeng1,2, Jianyu Xie1,2, Zhijie Yang1,2
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Scientific reports
|April 21, 2024
概括
本研究介绍了TO-UGDA,这是一种用于图域适应 (GDA) 的新框架,通过增强特征表示和下游适应来克服现有方法的局限性. 这种新方法提高了与未标记目标数据的节点级和图表级任务的性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 人工智能的人工智能
背景情况:
- 图域适应 (GDA) 面临的挑战是,目标图域中的标记数据有限.
- 现有的GDA方法通常仅依赖于表示对齐,它可能会受到不相关信息的影响,并忽略条件转移.
研究的目的:
- 提出一个面向目标的无监督图域自适应框架 (TO-UGDA),以有效地解决GDA的局限性.
- 提高标签信息从标记源域到未标记目标域的可转移性.
主要方法:
- 使用图形信息瓶提取域不变特征表示.
- 通过对抗对齐来最大限度地减少域差异,以实现统一的特征分布.
- 使用元伪标签来提高下游适应性和模型通用性.
主要成果:
- 拟议的TO-UGDA框架在各种节点级和图级适应任务中表现出色.
- 在现实世界的图形数据集上的实验验验证了框架的有效性.
结论:
- TO-UGDA为无监督图域适应提供了一个强大的解决方案.
- 该框架有效处理条件转移和无关信息,提高模型通用性.
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