Multi Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously and maximize the performance of one or all of these tasks. By sharing representations and optimizing resource utilization, this approach can improve the model's generalization ability and efficiency, reduce the risk of overfitting, and enhance the model's adaptability in complex scenarios. Multi-task learning has significant application value in fields such as natural language processing and computer vision.