WhiteCon：通过白化变换和双重一致性实现半监督域适应回归
WhiteCon: Semi-Supervised Domain Adaptation Regression Through Whitening Transform and Dual Consistency
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中文总结 AI 辅助
针对半监督域适应回归中标记目标数据有限的问题，提出WhiteCon方法，结合域特定白化变换与双重一致性正则化，稳定训练并提升域适应性能，在基准数据集上达到最先进水平。
中文摘要 AI 辅助
域适应对于解决跨域分布偏移导致模型性能下降的问题至关重要。尽管现有研究大多集中于分类任务，但针对连续输出任务的半监督域适应回归（SSDAR）在很大程度上仍未得到探索，尤其是在标记目标数据有限的实际情况中。为填补这一空白，我们提出了通过白化变换和双重一致性实现半监督域适应回归的方法（WhiteCon），该方法结合了域特定白化变换（DWT）和双重一致性正则化，以增强训练稳定性和域适应能力。DWT通过将特征协方差矩阵变换为单位矩阵来降低模型参数的方差，从而在普通最小二乘假设下稳定训练。此外，作为双重一致性正则化的一部分，方差一致性正则化对齐弱增强、强增强和混合增强特征的方差，以提高对增强引起的扰动的鲁棒性。在SSDAR设置下对各种基准数据集的实证评估表明，与现有方法相比，所提出的WhiteCon实现了最先进的性能，有效解决了回归任务中的域偏移问题。WhiteCon的代码可在以下网址获取：此https URL。
英文摘要
Domain adaptation is crucial for addressing distributional shifts that degrade model performance across domains. While most existing research has centered on classification, semi-supervised domain adaptation regression (SSDAR) for continuous-output tasks remains largely unexplored, particularly in practical scenarios with limited labeled target data. To address this gap, we propose semi-supervised domain adaptation regression through whitening transform and dual consistency (WhiteCon), which combines domain-specific whitening transform (DWT) and dual consistency regularization to enhance training stability and domain adaptation. DWT reduces the variance of the model parameters by transforming the feature covariance matrix into an identity matrix, thus stabilizing training under ordinary least squares assumptions. In addition, variance consistency regularization, as part of dual consistency regularization, aligns the variances of weak, strong, and mixup-augmented features to improve resilience against augmentation-induced perturbations. Empirical evaluations on various benchmark datasets under SSDAR settings demonstrate that the proposed WhiteCon achieves state-of-the-art performance compared to existing methods, effectively addressing domain shifts in regression tasks. The code for WhiteCon is available at https://github.com/sejin-sim/WhiteCon.
发表机构
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
