Design Perspectives of Multitask Deep Learning Models and Applications

TL;DR

This paper reviews the design, performance, and challenges of multi-task deep learning models, emphasizing feature sharing, task relationships, and applications across domains to improve model generalization and effectiveness.

Contribution

It provides a comprehensive visualization and comparison of existing multi-task models, analyzing their evaluation methods, design challenges, and domain-specific advantages.

Findings

Multi-task learning enhances model generalization across tasks.

Sharing features among related tasks improves performance.

Visualization helps understand model relationships and challenges.

Abstract

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