时间序列表示上的源自由通用域适应的综合基准

中文摘要 AI 辅助

源自由通用域适应（SF-UniDA）扩展了通用域适应，通过在适应阶段移除对源数据的访问，同时处理域之间的标签集不匹配。尽管对于图像数据，这一设置引起了越来越多的兴趣，但对于更具挑战性的时间序列，尚无基准存在。我们提出了首个时间序列上的SF-UniDA基准。此外，我们首次研究了预训练基础模型作为时间序列域适应特征提取器的性能。在此背景下，我们识别出所有现有SF-UniDA方法的一个关键且此前未被充分探索的局限性：用于未知样本拒绝的推断阈值高度敏感。我们通过提出一个即插即用的自动阈值模块来解决这一问题，该模块可集成到任何SF-UniDA方法中。在三个知名时间序列数据集上的实验证实了该模块的适用性。实验还强调，基础模型并未系统性地优于经典骨干网络，且针对时间序列定制的SF-UniDA仍有待开发。

英文摘要

Source-Free Universal Domain Adaptation (SF-UniDA) extends Universal Domain Adaptation by removing access to source data at adaptation time while still handling label-set mismatches between domains. Despite growing interest in this setting for image data, no benchmark exists for time series, which are more challenging. We present the first SF-UniDA benchmark on time series. In addition, we provide the first study of pretrained foundation models as feature extractors for time series domain adaptation. In this context, we identify a critical and previously underexplored limitation of all existing SF-UniDA methods: the inference threshold for unknown-sample rejection is highly sensitive. We address this by proposing a plug-in auto-thresholding module that can be integrated into any SF-UniDA method. Experiments on three well-known time series datasets confirm the suitability of this module. They also highlight that foundation models do not systematically outperform classical backbones and that SF-UniDA tailored for time series is yet to be developed.

发表机构

• Univ Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108（鲁昂大学、INSA鲁昂、勒阿弗尔大学、诺曼底大学、LITIS UR 4108）

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