Mitigating gradient conflicts via expert squads in multi-task learning

The foundation of multi-task learning lies in the collaboration and interaction among tasks. However, in numerous real-world scenarios, certain tasks usually necessitate distinct, specialized knowledge. The mixing of these different task-specific knowledge often results in gradient conflicts during the optimization process, posing a significant challenge in the design of effective multi-task learning systems. This study proposes a straightforward yet effective multi-task learning framework that employs groups of expert networks to decouple the learning of task-specific knowledge and mitigate such gradient conflicts. Specifically, this approach partitions the feature channels into task-specific and shared components. The task-specific subsets are processed by dedicated experts to distill specialized knowledge. The shared features are captured by a point-wise aggregation layer from the whole outputs of all experts, demonstrating superior performance in capturing inter-task interactions. By considering both task-specific knowledge and shared features, the proposed approach exhibits superior performance in multi-task learning. Extensive experiments conducted on the PASCAL-Context and NYUD-v2 datasets have demonstrated the superiority of the proposed approach compared to other state-of-the-art methods. Furthermore, a benchmark dataset for multi-task learning in underwater scenarios has been developed, encompassing object detection and underwater image enhancement tasks. Comprehensive experiments on this dataset consistently validate the effectiveness of the proposed multi-task learning strategy. The source code is available at https://github.com/chenjie04/Multi-Task-Learning-PyTorch.