综合解与细粒度的特征缓解域泛化域的综合解.
Youjia Shao1, Changshuo Wang2, Qihang Jia1
1College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
概括
这项研究引入了域概括的新方法,它将特征分离出来,以提高在未见数据上的模型性能. 该方法通过将域不变特征和域特定特征分开来增强概括性,以实现更稳定的学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 域泛化解决了域转移的挑战,在其中,在一个数据分布上训练的模型在其他数据分布上失败.
- 现有的方法在域不变表示学习过程中经常与不稳定的域特定特征作斗争.
研究的目的:
- 开发一种新的方法,在域泛化中实现全面和明确的特征解.
- 通过减少对虚假域特定特征的依赖和增强稳定的语义来提高模型的概括能力.
主要方法:
- 拟议的源分割流解与光滑细粒度特征缓解 (SSDS-FFM) 范式.
- 实现了一个源分流结构,具有独立的域不变和域特定特征提取器.
- 利用相互信息最小化,域名标签光滑以及选择性反向对比学习来实现特征解和增强.
主要成果:
- 实现了全面的特征解,有效地将域不变特征和域特定特征分开.
- 通过稳定的域不变表示学习,表现出更好的概括能力.
- 在基准数据集上验证的有效性和优势:PACS,VLCS,Office-Home和DomainNet.
结论:
- 拟议的SSDS-FFM范式提供了一个逻辑方法来实现强大的域泛化.
- 综合功能解导致在未见域上增强模型性能.
- 该方法成功地减轻了局部域错位和特征空间压缩.
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