Semi Supervised Transfer

Semi-supervised transfer learning aims to leverage labeled data from a source domain and unlabeled data from a target domain to improve model performance on the target domain, particularly when labeled target data is scarce. Current research focuses on developing novel regularization techniques, such as information-theoretic approaches and expected statistic regularization, to effectively transfer knowledge across domains and mitigate distribution discrepancies. These methods are applied across various tasks, including natural language processing (e.g., cross-lingual parsing, argument mining) and speech recognition, demonstrating significant improvements in model accuracy and efficiency for low-resource scenarios. This approach holds considerable promise for advancing machine learning applications in domains with limited annotated data.