Source Target

Source-target research focuses on transferring knowledge or models learned from a source dataset to a target dataset, aiming to improve performance on the target data, especially when labeled target data is scarce or expensive to obtain. Current research emphasizes strategies to optimize this transfer, including developing similarity and diversity metrics to guide model selection and employing federated learning techniques to handle decentralized and heterogeneous data across multiple sources and targets. This work is significant because it enables efficient model training and deployment in various applications, such as time series forecasting, medical image analysis, and object detection, by leveraging existing data and reducing the need for extensive new data collection.