An Overview of Multi-Task Learning in Deep Neural Networks
It is an incremental overview article aimed at helping ML practitioners understand and implement MTL across various domains.
This paper provides a general overview of multi-task learning (MTL) in deep neural networks, introducing common methods, reviewing literature, and offering guidelines for practitioners to apply MTL effectively.
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.