Multi Tasking Model

Multi-task learning (MTL) aims to train a single model to perform multiple related tasks simultaneously, improving efficiency and generalization compared to training separate models for each task. Current research focuses on optimizing model architectures, such as incorporating Mixture of Experts (MoE) modules for dynamic task-specific knowledge integration and employing techniques like contrastive learning to handle data variability across tasks. These advancements are improving performance in diverse applications, including medical image analysis, robotic surgery, and human-robot interaction, by enabling more efficient and robust solutions to complex problems.