Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation

作者：Yucheng Wang, Peiliang Gong, Manxio Wu, Felix Ott, Xiaoli Li, Lihua Xie, Zhenghua Chen · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2026 · DOI：10.1109/tpami.2026.3681762 · 被引用次数：1 · 研究领域：Speech and Audio Processing、Domain Adaptation and Few-Shot Learning、Seismic Imaging and Inversion Techniques

Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adaptation (UDAs) offers an effective solution, growing data privacy concerns have led to the development of Source-Free UDA (SFUDAs), enabling model adaptation to target domains without accessing source data. Despite their potential, applying existing SFUDAs to TS data is challenging due to the difficulty of transferring temporal dependencies-an essential characteristic of TS data-particularly in the absence of source samples. Although prior works attempt to address this by specific source pretraining designs, such requirements are often impractical, as source data owners cannot be expected to adhere to particular pretraining schemes. To address this, we propose Temporal Source Recovery (TemSR), a framework that leverages the intrinsic properties of TS data to generate a source-like domain and recover source temporal dependencies. With this domain, TemSR enables dependency transfer to the target domain without accessing source data or relying on source-specific designs, thereby facilitating effective and practical TS-SFUDA. TemSR features a masking-recovery-optimization process to generate a source-like distribution with restored temporal dependencies. This distribution is further refined through local context-aware regularization to preserve local dependencies, and anchor-based recovery diversity maxim...