论文标题
对深度多任务学习和辅助任务学习的简要回顾
A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
论文作者
论文摘要
多任务学习（MTL）同时优化了几个学习任务，并利用其共享信息来改善概括和每个任务的模型预测。可以将辅助任务添加到主要任务中，以最终提高性能。在本文中，我们对最近的深层多任务学习（DMTL）方法进行了简短的审查，然后是选择有用的辅助任务的方法，这些任务可在DMTL中使用，以提高模型的主要任务。
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task. Auxiliary tasks can be added to the main task to ultimately boost the performance. In this paper, we provide a brief review on the recent deep multi-task learning (dMTL) approaches followed by methods on selecting useful auxiliary tasks that can be used in dMTL to improve the performance of the model for the main task.
