Multi-task learning is a machine learning approach in which multiple tasks are learned jointly, instead of being learned separately. This can be used to improve the performance of all tasks by sharing information between them. For example, if two tasks are closely related, then learning them together can help the model learn features that are relevant to both tasks. Additionally, multi-task learning can be used to reduce the amount of data needed to train a model – if multiple tasks share the same input data, then the model only needs to be trained on that data once.
There are many ways toapproach multi-task learning, and the choice of method will depend on the type of tasks and data involved. Some common methods include shared feature representation, task specific feature representation, multi-task objectives, and multi-task architecture. Additionally, hybrid methods – which combine two or more of the aforementioned approaches – are also popular.
References:
https://en.wikipedia.org/wiki/Multi-task_learning
https://machinelearningmastery.com/multi-task-machine-learning-algorithms/
https://towardsdatascience.com/multitask-learning-for-tabular-data-with-deep-neural-networks-2dd1b6575b46
https://www.analyticsvidhya.com/blog/2017/05/learning-from-multiple-tasks/https://github.com/awslabs/multi-task-learning