异质域适应与一般化相似性和不相似性规范化
IEEE transactions on neural networks and learning systems
|March 11, 2024
概括
使用矩阵因子化的异质域适应 (HDA) 方法得到了HGSDR的改进. 这种新的方法利用标签信息来增强跨领域的相似性和可分离性,从而产生更具歧视性的特征.
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:
- 异质域适应 (HDA) 处理源域和目标域之间具有不同特征表示的转移学习.
- 现有的矩阵分解HDA方法有效地学习可转移的特征,但忽视跨域样本相似性和可分离性的标签信息.
- 这种限制导致了跨领域的偏见和共同子空间中的潜在类混合,阻碍了歧视性的目标特征学习.
研究的目的:
- 提出一种新的基于矩阵因子化的HDA方法,HGSDR (HDA与一般化相似性和不相似性规范化).
- 通过结合标签信息来改善跨领域的特征学习,以探索样本相似性和可分离性.
- 通过减轻跨域偏差来增强目标域特征的区分能力.
主要方法:
- 开发了一种基于矩阵分解的HDA方法HGSDR,该方法包含了概括的相似性和不相似性规范化.
- 引入了一个相似性调节器,使用跨域拉普拉斯图和标签信息来捕获相同的跨域类之间的相似性.
- 提出基于内部积的不相似性调节器,以增加不同跨域类之间的分离性,同时保留未标记的目标样本的邻居关系.
主要成果:
- HGSDR有效地匹配全球和样本级域名分布.
- 与现有方法相比,该方法对目标样本学习了更多的歧视性特征.
- 对基准数据集的广泛实验验证实了HGSDR在最先进的方法上的优越性.
结论:
- 通过利用标签信息来增强相似性和不相似性的规范化,HGSDR显著改善了异质域的适应性.
- 拟议的方法有效地减少了跨域偏差,并学习了目标域的歧视性特征.
- HGSDR提供了一种优越的方法来应对HDA挑战,特别是在处理异质特征和有限的标记目标数据时.
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