Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt a model from a labeled source domain to an unlabeled target domain for regression tasks. Recent successful works in UDAR mostly focus on subspace alignment, involving the alignment of a selected subspace within the entire feature space. This contrasts with the feature alignment methods used for classification, which aim at aligning the entire feature space and have proven effective but are less so in regression settings. Specifically, while classification aims to identify separate clusters across the entire embedding dimension, regression induces less structure in the data representation, necessitating additional guidance for efficient alignment. In this paper, we propose an effective method for UDAR by incorporating guidance from uncertainty. Our approach serves a dual purpose: providing a measure of confidence in predictions and acting as a regularization of the embedding space. Specifically, we leverage the Deep Evidential Learning framework, which outputs both predictions and uncertainties for each input sample. We propose aligning the parameters of higher-order evidential distributions between the source and target domains using traditional alignment methods at the feature or posterior level. Additionally, we propose to augment the feature space representation by mixing source samples with pseudo-labeled target samples based on label similarity. This cross-domain mixing strategy produces more realistic samples than random mixing and introduces higher uncertainty, facilitating further alignment. We demonstrate the effectiveness of our approach on four benchmarks for UDAR, on which we outperform existing methods.

翻译：无监督域适应回归（UDAR）旨在将模型从有标签的源域适配到无标签的目标域以完成回归任务。近期UDAR领域的成功工作主要集中于子空间对齐，即对齐整个特征空间中选定的子集。这与分类任务中使用的特征对齐方法形成对比——分类方法旨在对齐整个特征空间，虽然已被证明有效，但在回归场景中表现较弱。具体而言，分类任务需要识别整个嵌入维度中的独立聚类簇，而回归任务导致数据表示结构较弱，因此需要额外引导才能实现高效对齐。本文提出一种通过不确定性引导的UDAR有效方法。该方法具有双重功能：一方面提供预测置信度的度量，另一方面对嵌入空间进行正则化。具体地，我们利用深度证据学习框架，该框架可输出每个输入样本的预测值与不确定性。我们提出通过在特征或后验层面使用传统对齐方法，对齐源域与目标域的高阶证据分布参数。此外，我们提出通过基于标签相似性混合源样本与伪标签目标样本，增强特征空间表示。这种跨域混合策略比随机混合能生成更真实的样本，并引入更高不确定性，从而促进进一步对齐。我们在四个UDAR基准数据集上验证了方法的有效性，结果表明我们的方法优于现有方法。