Target Domain Adaptation

Target domain adaptation aims to transfer knowledge learned from a source domain to a different, but related, target domain where labeled data is scarce or unavailable. Current research emphasizes source-free adaptation, where only a pre-trained model from the source domain is accessible, and addresses challenges like blended target domains (multiple target domains mixed together) and partial-set domains (where the target domain has a subset of classes present in the source). Methods often involve adversarial learning, contrastive learning, and ensemble techniques to improve model robustness and reduce the impact of distribution shifts between domains. This field is crucial for improving the efficiency and generalizability of machine learning models, particularly in scenarios with limited labeled data in the target domain, such as remote sensing or medical imaging.