CALDA: Improving Multi-Source Time Series Domain Adaptation With Contrastive Adversarial Learning.

ABSTRACT: Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA p

SUBMITTER: Wilson G 

PROVIDER: S-EPMC10805953 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature