双视图全球和本地类别关注域调整,用于无监督的条件对抗域调整
1School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
概括
本研究引入了一种新的无监督域名适应方法,即双视图全球和本地类别专注域名对齐 (DV-GLCA),以改善特征对齐和目标域名可区分性. DV-GLCA提高了无监督域调整任务的稳定性和性能.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 无监督域调整 (UDA) 旨在将模型调整为没有标记数据的新域.
- 条件对抗域调整 (CADA) 使用多式联络信息对准源域和目标域的分布.
- 现有的CADA方法与不准确的多式联运信息作斗争,导致分布不匹配和目标域差异化.
研究的目的:
- 提出一个新的无监督的CADA框架,DV-GLCA,以解决现有方法的局限性.
- 改善源域和目标域之间的多式联运结构的调整.
- 增强目标域内的特征的可区分性.
主要方法:
- 将双视图信息集成到条件对抗域调整中 (DV-CADA).
- 全球类别-注意域对齐 (GCA) 使用编码速率降低和双视图中心点对齐.
- 当地类别-注意域对齐 (LCA) 采用对比域差异对模糊样本.
主要成果:
- DV-GLCA减轻了分布不匹配,并产生了更强大的域不变特征.
- 拟议的GCA和LCA组件增强了目标域特征的可区分性.
- 该框架在五个UDA基准中取得了领先的表现.
结论:
- 在无监督域适应方面,DV-GLCA提供了显著的进步.
- 双视图方法和关注类别的机制有效地改善了特征对齐和可区分性.
- 该方法在各种UDA场景中表现出强大的有效性和稳定性.
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