适应式图形学习具有语义推广性,用于域调整
概括
本研究介绍了具有语义推广性的自适应图形学习 (AGLSP),以改善域名适应. AGLSP有效地捕获了个性化和本地知识,增强了跨领域的知识传输,以提高模型性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 域调整 (DA) 旨在弥合标记源域和未标记目标域之间的差距.
- 现有的基于语义的DA方法经常与个性化的类内和本地类间知识获取作斗争.
- 这种限制阻碍了跨领域的全面知识转移.
研究的目的:
- 提出一种新的域调整方法,即具有语义推广性的自适应图形学习 (AGLSP).
- 解决在跨领域环境中获得个性化和本地知识的局限性.
- 增强源域和目标域之间语义知识的转移.
主要方法:
- AGLSP使用自适应图形嵌入与语义指导 (AGE-SG) 来估计目标样本的推广性,并学习特定领域的组件.
- 语义上可推广的样本增强 (SPSE) 提炼了使用多粒度的类内和目标样本挖掘的特征区分能力.
- 具有隐含语义保存的自适应图形学习 (AGL-ISP) 提取标签细分性的共同点,即使是从不可推广的目标样本.
主要成果:
- 拟议的AGLSP方法在域调整任务中表现出卓越的性能.
- 在七个数据集上进行了广泛的实验,验证了AGLSP方法的有效性.
- 通过学习更丰富的语义信息,AGLSP成功地转移了更多的跨领域知识.
结论:
- 通过解决现有方法的局限性,AGLSP为域调整提供了一个强大的框架.
- 多语义细分化和以目标样本为导向的策略显著改善了知识转移.
- 这种方法显示出各种跨领域学习应用的巨大潜力.
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