Target Source

Target source research focuses on leveraging information from readily available source data to improve performance on tasks with limited target data. Current approaches utilize multi-task learning, employing techniques like LASSO regularization to select optimal source tasks and optimal condition training to refine the use of multiple, potentially overlapping, source conditions. These methods are applied across diverse fields, including speech recognition, image generation, and computer vision, demonstrating improvements in accuracy and efficiency by effectively transferring knowledge from abundant source domains to scarce target domains. The resulting advancements contribute to more robust and data-efficient machine learning models across various applications.