Intelligent Dependency and its Cracking Path Combining Transfer Learning and Multi task Learning

This paper explores a new approach that combines transfer learning and multi-task learning to solve the dependency problem in intelligent systems. The study first analyzes the intelligent dependency problems in existing applications of transfer learning and multitask learning, including the insufficient generalization ability of models and the limitations of inter-task knowledge transfer. Through a detailed review of related literature, this paper identifies the shortcomings of current approaches in dealing with complex multitasking environments and proposes Combined Multi-Task Model (CMTM) with transfer learning, which aims to optimize the knowledge transfer and task learning efficiency through an innovative learning framework. Among these data conclusions, the combination of transfer learning and multitasking learning models shows significant advantages in improving the model's generalization ability and efficiency.