  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 pulls examples with the same label close to each other,
while pushing apart examples with different labels, reshaping the space through
contrastive learning. Unlike prior contrastive adaptation methods, CALDA
requires neither data augmentation nor pseudo labeling, which may be more
challenging for time series. We empirically validate our proposed approach.
Based on results from human activity recognition, electromyography, and
synthetic datasets, we find utilizing cross-source information improves
performance over prior time series and contrastive methods. Weak supervision
further improves performance, even in the presence of noise, allowing CALDA to
offer generalizable strategies for MS-UDA. Code is available at:
https://github.com/floft/calda
