Unlabeled Target

Unlabeled target domain adaptation focuses on improving the performance of machine learning models on new, unlabeled datasets without access to the original training data or only with limited labeled target data. Current research emphasizes self-training methods, often incorporating techniques like pseudo-labeling, metric learning, and mutual information maximization to leverage the unlabeled target data and mitigate distribution shifts between source and target domains. This area is crucial for addressing real-world scenarios where labeled data is scarce or expensive, improving the generalizability and robustness of machine learning models across diverse applications, such as medical image analysis and document understanding. The development of effective strategies for handling unlabeled target data is vital for advancing the practical applicability of machine learning.