The transfer of models trained on labeled datasets in a source domain to unlabeled target domains is made possible by unsupervised domain adaptation (UDA). However, when dealing with complex time series models, the transferability becomes challenging due to the dynamic temporal structure that varies between domains, resulting in feature shifts and gaps in the time and frequency representations. Furthermore, tasks in the source and target domains can have vastly different label distributions, making it difficult for UDA to mitigate label shifts and recognize labels that only exist in the target domain. We present RAINCOAT, the first model for both closed-set and universal DA on complex time series. RAINCOAT addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally,RAINCOAT improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that RAINCOAT can achieve an improvement in performance of up to 16.33%, and can effectively handle both closed-set and universal adaptation.
翻译:在源域的标签数据集培训模型向无标签目标域的转让之所以成为可能,是因为未受监督的域域适应(UDA)所致。然而,在处理复杂的时间序列模型时,由于不同领域的动态时间结构不同,导致时间和频率表达方式的特征变化和差距,因此可转让性变得具有挑战性。此外,源域和目标域的任务可能具有巨大的不同标签分布,使UDA难以减缓标签的转移和识别仅在目标域存在的标签。我们介绍了REAINCOAT,这是在复杂时间序列中封闭式和通用的DA的第一个模型。REAINCOAT通过考虑时间和频率特征的特征和频率特征变化,对时间和频率特点进行调整,纠正不匹配,以便利发现私人标签。此外,RAINCOAT通过确定目标域的标签变化,提高可转让性。我们用5个数据集和13个最先进的UDAA方法进行的实验表明,RAINCOAT能够改进最高达16.33%的性能,并能够有效地处理封闭式和普遍适应。