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

Understand

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.