Task Specific Weight

Task-specific weighting in multi-task learning aims to optimize the contribution of individual tasks during model training, addressing the challenge of conflicting gradients and improving overall performance. Current research focuses on developing dynamic weighting schemes, often employing gradient projection, uncertainty estimation, or attention mechanisms, to adaptively adjust task importance based on factors like task difficulty, convergence progress, or even sample-level characteristics. These advancements enhance the efficiency and robustness of multi-task models across diverse applications, from natural language processing and computer vision to robotics and medical image analysis. The resulting improvements in model accuracy and resource utilization have significant implications for various fields.