Confidence Sample Selection and Specific Feature Enhancement for Domain Adaptation

Teng Shaohua; Wu Zefeng; Teng Luyao; Zhang Wei; Zeng Ying

doi:10.12052/gdutxb.230188

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Confidence Sample Selection and Specific Feature Enhancement for Domain Adaptation

更新时间：2026-05-27

- Confidence Sample Selection and Specific Feature Enhancement for Domain Adaptation
- Vol. 42, Issue 3, Pages: 12-26(2025)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广州番禺职业技术学院信息工程学院,广东,广州,511483
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230188
  CLC： TP181
- Published：2025
- 稿件说明：
移动端阅览
滕少华, 吴泽锋, 滕璐瑶, 等. 置信样本选择与差异性特征增强的域适应[J]. 广东工业大学学报, 2025, 42(3): 12–26.

Teng Shaohua, Wu Zefeng, Teng Luyao, et al. Confidence Sample Selection and Specific Feature Enhancement for Domain Adaptation[J]. 2025, 42(3): 12-26.
滕少华, 吴泽锋, 滕璐瑶, 等. 置信样本选择与差异性特征增强的域适应[J]. 广东工业大学学报, 2025, 42(3): 12–26. DOI： 10.12052/gdutxb.230188

Teng Shaohua, Wu Zefeng, Teng Luyao, et al. Confidence Sample Selection and Specific Feature Enhancement for Domain Adaptation[J]. 2025, 42(3): 12-26. DOI： 10.12052/gdutxb.230188.

摘要

域适应学习因其有效减少域差异实现标签传播被广泛应用。目前，大多数域适应学习仅通过线性判别分析增强线性数据的判别性而忽略了现实世界存在非线性数据；同时，这些方法未考虑目标域的低置信样本对训练过程的负影响。因此，本文提出一种新颖的置信样本选择与差异性特征增强的域适应框架(Confidence Sample Selection and Specific Feature Enhancement

CSS-SFE)。首先，该框架通过最小最大原则选择高置信的目标样本来辅助训练，减少不正确伪标签影响；其次，权衡类散点矩阵和邻居散点矩阵的贡献来增强线性和非线性数据集的特征，提高数据集的判别性；接着，分别为源域和目标域的样本学习不同投影矩阵来保持各自的差异性特征，防止源域和目标域的样本区分性降低；再次，进一步应用边缘分布对齐和条件分布对齐减少域分布差异；最后，在多个基准数据集上进行的广泛实验证明该方法优于目前的方法。

Abstract

For domain shifts

domain adaptation (DA) promotes label propagation by reducing distributional differences. In recent work

DA only enhances the discriminability of linear data by linear discriminant analysis

which ignores real-world nonlinear data. Meanwhile

these methods fail to consider the negative impact of the target low-confidence samples on the training process. Therefore

confidence sample selection and specific feature enhancement for domain adaptation (CSS-SFE) is proposed. Firstly

the framework selects target high-confidence samples through the min-max principle to reduce the impact of incorrect pseudo-label during training; Secondly

the class scatter matrix and neighbor scatter matrix are balanced to enhance the features of linear and nonlinear datasets so that the discriminability of the samples is improved; Thirdly

the framework maintains source and target specific features by learning different projection matrices

which prevents the discriminability of the samples decreasing; Fourthly

the marginal and conditional distribution alignment is further applied to reduce the domain distribution discrepancy; Finally

extensive experiments on several benchmark datasets demonstrate the superiority of CSS- SFE over state-of-the-art methods.

关键词

Keywords

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