计算机科学
特征(语言学)
集合(抽象数据类型)
可转让性
适应(眼睛)
系列(地层学)
学习迁移
领域(数学分析)
人工智能
域适应
模式识别(心理学)
机器学习
数学
数学分析
哲学
物理
罗伊特
光学
古生物学
分类器(UML)
生物
程序设计语言
语言学
作者
Huan He,Owen Queen,Teddy Koker,Consuelo Cuevas,Theodoros Tsiligkaridis,Marinka Žitnik
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:11
标识
DOI:10.48550/arxiv.2302.03133
摘要
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation 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 improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation.
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