发表机构
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出CPDA框架，通过对齐类条件潜在路径分布而非全局特征边际分布，在13个时间序列DA基准上验证其优于30种基线方法，实现无监督时间序列领域自适应的性能提升。
AI 中文摘要
无监督时间序列领域自适应（DA）解决在由不同用户、传感器、设备、采集条件或时间动态引起的分布偏移下，将分类器从有标记源域迁移到无标记目标域的挑战。现有方法通常通过对抗训练、最优传输或基于矩的差异来对齐边际特征分布以缓解该偏移。本文提出类条件路径分布对齐（CPDA），这是一种非对抗性的基于差异的框架，用于对齐源域和目标域的类条件潜在路径分布，而非仅全局特征边际分布。CPDA引入复合签名-谱核，共同捕获池化语义特征、时间路径结构、频域信息和低秩路径签名动态，同时利用源域标签和目标域软伪标签执行类保留对齐。我们进一步提供理论分析，表明CPDA定义了有效核差异，将现有矩匹配方法作为受限情况，并得到类条件目标风险界。在13个不同的时间序列DA基准上使用CNN、ResNet18和TCN骨干网络进行的大量实验，证明了CPDA相对于30个差异、对抗和伪标签基线的有效性。
英文摘要
Unsupervised time-series domain adaptation (DA) addresses the challenge of transferring a classifier from a labeled source domain to an unlabeled target domain under distribution shifts induced by different users, sensors, devices, acquisition conditions, or temporal dynamics. Existing methods typically mitigate this shift by aligning marginal feature distributions through adversarial training, optimal transport, or moment-based discrepancies. In this paper, we propose Class-Conditional Path Distribution Alignment (CPDA), a non-adversarial discrepancy-based framework that aligns source and target class-conditional latent path distributions rather than only global feature marginals. CPDA introduces a composite signature-spectral kernel that jointly captures pooled semantic features, temporal path structure, frequency-domain information, and low-rank path-signature dynamics, while using source labels and target soft pseudo-labels to perform class-preserving alignment. We further provide a theoretical analysis showing that CPDA defines a valid kernel discrepancy, admits existing moment-matching methods as restricted cases, and yields a class-conditional target-risk bound. Extensive experiments with CNN, ResNet18, and TCN backbones on 13 different time-series DA benchmarks demonstrate the effectiveness of CPDA against 30 discrepancy, adversarial, and pseudo-labeling baselines.
Comments34 pages, 7 figures
