ESSR: Evolving Sparse Sharing Representation for Multitask Learning
提出一种演化方法，通过嵌入神经网络优化到演化多任务中，自适应学习稀疏共享表示，识别并移除负相关和冗余特征，为每个任务生成最优稀疏共享子网络，提升多任务学习性能并减小推理模型。
Multi-task learning uses knowledge transfer among tasks to improve the generalization performance of all tasks. For deep multi-task learning, knowledge transfer is often implemented via sharing all hidden features of tasks. A major shortcoming is that it can lead to negative knowledge transfer across tasks when task correlation is weak. To overcome it, this paper proposes an evolutionary method to learn sparse sharing representations adaptively. By embedding the neural network optimization into evolutionary multitasking, our proposed method finds an optimal combination of tasks and sharing features. It can identify negative correlation and redundant features and then remove them from the hidden feature set. Thus, an optimal sparse sharing subnetwork can be produced for each task. Experiment results show that the proposed method achieve better learning performance with a smaller inference model than other related methods.