Unlabeled Target Domain

Unlabeled target domain adaptation focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, aiming to improve model performance in the target domain without requiring its data to be labeled. Current research emphasizes techniques like optimal transport, adversarial training, and knowledge distillation, often implemented within deep learning frameworks, to bridge the domain gap and mitigate issues like negative transfer and unreliable pseudo-labels. This field is crucial for addressing real-world scenarios where labeled target data is scarce or expensive to obtain, impacting diverse applications such as medical image analysis, fault diagnosis, and object detection across different visual domains. The development of robust and reliable evaluation metrics for unlabeled target domains is also a significant area of ongoing investigation.