张量式多视图低级高级图形学习,用于上下文增强的域调整
Chenyang Zhu1, Lanlan Zhang1, Weibin Luo1
1School of Computer Science and Artificial Intelligence, Changzhou University, China.
概括
本研究介绍了一种新的机器学习框架,即Tensorial Multiview低级高级图形学习 (MLRGL),用于无监督域适应 (UDA). MLRGL有效地捕捉了上下文关系,在UDA任务中表现优于现有的方法.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 无监督域调整 (UDA) 将知识从被标记的域转移到未被标记的域.
- 当前的UDA方法与目标域的上下文关系作斗争.
- 域间的分布转移是一个重大挑战.
研究的目的:
- 引入一个新的框架,Tensorial多视图低级高级图形学习 (MLRGL),用于UDA.
- 改进在目标领域捕获和利用上下文关系.
- 增强用于UDA任务的域不变特征学习.
主要方法:
- 学习由低级张量所限制的高阶图形,以揭示上下文关系.
- 使用空间上下文和增强掩饰生成多视图域不变特征.
- 通过组合拉普拉斯图来构建一个高阶图,以进行特征传播.
- 应用低级约束对面试和类间相关性发现.
- 使用原型向量和无监督聚类来计算条件概率.
主要成果:
- 与基准数据集中的最先进方法相比,MLRGL框架显示出更高的性能.
- 拟议的方法显示出对超参数变化的稳定性.
- 在MLRGL中的多视图学习策略优于单视图解决方案.
结论:
- 通过利用高阶图形学习和低级张量约束,MLRGL有效地解决了现有的UDA方法的局限性.
- 该框架成功地揭示和利用上下文关系,以改善域名适应.
- 多视图功能学习是推动UDA研究的一个有希望的方向.
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