Can small heads help? understanding and improving multi-task generalization

摘要: Multi-task learning aims to solve multiple machine learning tasks at the same time, with good solutions being both generalizable and Pareto optimal. A multi-task deep learning model consists of a shared representation learned to capture task commonalities, and task-specific sub-networks capturing the specificities of each task. In this work, we offer insights on the under-explored trade-off between minimizing task training conflicts in multi-task learning and improving multi-task generalization, i.e. the generalization capability of the shared presentation across all tasks. The trade-off can be viewed as the tension between multi-objective optimization and shared representation learning: As a multi-objective optimization problem, sufficient parameterization is needed for mitigating task conflicts in a constrained solution space; However, from a representation learning perspective, over-parameterizing the task-specific sub …