Contrastive Domain Adaptation

Contrastive domain adaptation (CDA) aims to improve the performance of machine learning models on a target domain with limited or no labeled data by leveraging labeled data from a related source domain. Current research focuses on developing CDA methods that utilize contrastive learning, often incorporating techniques like maximum mean discrepancy (MMD) and various forms of contrastive loss functions, within diverse model architectures tailored to specific applications (e.g., multi-branch networks, self-supervised pre-training). This approach is proving valuable across numerous fields, including medical image analysis, natural language processing, and time-series analysis, by enabling more robust and adaptable models in scenarios with limited annotated data.