Deep Feature Unsupervised Domain Adaptation for Time-Series Classification

Unsupervised domain adaptation (UDA) for time series classification (TSC) is an important but challenging task. In the process of UDA, feature learning is most critical. Most of the existing works in this area are based on learning domain-invariant feature representation of data with help of some restriction such as MMD. However, they ignored that the mutual effects between the pretrained network and the downstream target network was also conducive to the learning of domain-invariant features. In this article, we propose a deep feature unsupervised domain adaptation (DFUDA) for time series classification. First, we pretrain a network based on consistency learning to ensure the invariant feature extraction from the source and target domains. Then, we propose an end-to-end unsupervised domain adaptation, which includes the layer matching and the unsupervised domain adaptation to promote more confident knowledge transfer. Finally, the pretrained network receives feedback of the domain adaptation's performance. To verify the effectiveness of the proposed method, we perform the comprehensive experiments on fault diagnosis datasets and human activity recognition datasets. The results show that DFUDA outperforms the state of the arts methods for both scenarios.