In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under learning-related tasks, multi-task learning has been able to generalize the models even better. We try to enhance the feature mapping of the multi-tasking models by sharing features among related tasks and inductive transfer learning. Also, our interest is in learning the task relationships among various tasks for acquiring better benefits from multi-task learning. In this chapter, our objective is to visualize the existing multi-tasking models, compare their performances, the methods used to evaluate the performance of the multi-tasking models, discuss the problems faced during the design and implementation of these models in various domains, and the advantages and milestones achieved by them
翻译:近年来,多任务学习在各种应用中取得了巨大成功。虽然单一模式培训在过去几年中承诺取得巨大成果,但忽视了可能有助于我们更好地估计衡量尺度的宝贵信息。在与学习有关的任务中,多任务学习能够更全面地推广模型。我们试图通过在相关任务中分享特征和感化性转移学习来加强多任务模式的特征制图。我们还有兴趣学习各种任务之间的任务关系,以便从多任务学习中获得更好的好处。在本章中,我们的目标是将现有的多任务模式直观化,比较其绩效,用来评估多任务模式绩效的方法,讨论这些模式在各领域设计和执行过程中遇到的问题,以及这些模式的优势和里程碑。