摘要
领域自适应旨在利用带标签的源域数据和无标签的目标域数据来解决机器学习泛化性不足的问题.现有领域自适应工作主要针对计算机视觉任务.为了解决针对时间序列数据的领域自适应挑战,现有的方法将针对图片数据的方法直接应用于时间序列数据中.这些方法虽然一定程度上解决了模型的泛化能力,但是这些方法依然不能很好地提取解耦的领域不变的特征,从而使得模型的泛化性能依然不尽人意.为了解决这个挑战,提出基于隐变量解耦学习的无监督领域自适应算法.首先,提出针对时间序列数据的因果数据生成过程,在这个生成过程中,假设观测数据背后的隐变量分为变化部分和不变部分,并且将这些部分用隐变量表示.基于这个数据生成过程,提出可识别性理论证明领域变化的隐变量是可以被识别的.在可识别性理论的基础上,设计针对时间序列的隐变量解耦学习领域自适应模型(time series domain adaptation via disentangling invariant and variant latent variables,DIVV).该模型一方面利用变分推断解耦领域变化的隐变量,另一方面采用基于正交特征的对齐模块以解耦领域不变的隐变量.最后该模型采用领域不变特征进行时间序列分类.在多个真实数据集上进行验证,并且取得了最有效的实验结果,证明所提理论和模型在真实场景中的有效性.
Since the currently popular deep learning models are often influenced by the notorious phenomenon known as distribution shift,domain adaptation has been proposed to enhance the generalization of these models,transferring knowledge from labeled source data to unlabeled target data.Existing methods for domain adaptation primarily focus on computer vision tasks,leading to the application of models devised for image data to time series data to address the domain adaptation problem for time series data.Although these methods mitigate distribution shift to some extent,they struggle to effectively extract disentangled domain-invariant representations for time series data,resulting in suboptimal performance.To address this issue,a disentangled invariant and variant latent variable model for time series domain adaptation(DIVV)is proposed.Specifically,a causal generation process for time series data is introduced,where the latent variables are partitioned into domain-specific and domain-invariant latent variables.Based on this data generation process,the identifiability of domain-specific latent variables is established.The DIVV model,built on this identification theory,disentangles domain-specific and domain-invariant latent variables using variational influence and an orthogonal basis alignment module.Finally,the DIVV model leverages domain-invariant representations for time series classification.Experimental results demonstrate that the DIVV model outperforms existing domain adaptation methods for time series data across various benchmark datasets,highlighting its effectiveness in real-world applications.
作者
李梓健
蔡瑞初
陈浩芝
姜志帆
陈道鑫
郝志峰
LI Zi-Jian;CAI Rui-Chu;CHEN Hao-Zhi;JIANG Zhi-Fan;CHEN Dao-Xin;HAO Zhi-Feng(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China;College of Engineering,Shantou University,Shantou 515063,China)
出处
《软件学报》
北大核心
2025年第12期5554-5571,共18页
Journal of Software
基金
科技创新2030—“新一代人工智能”重大项目(2021ZD0111501)
国家自然科学基金优秀青年科学基金(62122022)
国家自然科学基金(62206064)。
关键词
迁移学习
时间序列领域自适应
时间序列分类
因果数据生成过程
可识别性
transfer learning
time series domain adaptation
time series classification
causal generation process
identifiability
