  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
