Weight Transfer

Weight transfer, a technique in machine learning, aims to leverage knowledge learned from a source domain to improve model performance in a target domain, mitigating the need for extensive retraining and addressing data scarcity or privacy concerns. Current research focuses on refining weight transfer methods within various architectures, including transformers and recurrent neural networks, exploring strategies like regularization and incremental learning to prevent catastrophic forgetting and improve generalization. These advancements are impacting diverse fields, from medical image analysis (e.g., improving segmentation accuracy in CBCT scans) to resource-constrained applications like hospital length-of-stay prediction, where efficient model adaptation is crucial. The ultimate goal is to develop robust and efficient transfer learning techniques applicable across various domains and data types.