Lihua Xie

Advancing Imbalanced Domain Adaptation: Cluster-Level Discrepancy Minimization With a Comprehensive Benchmark

2021 · IEEE Transactions on Cybernetics

Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume …

SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, …