Multi-Task Learning
Multi-Task Learning (MTL) is an approach where a single model is trained to perform multiple related tasks simultaneously. By sharing representations across tasks, the model can leverage commonalities and improve generalization, especially useful when tasks are similar or data for one task is limited. This often leads to better performance than training separate models.
It's like sending one student to a library to research for three related assignments at once, instead of sending three separate students. The student might find overlapping information or insights that benefit all assignments.
It improves model efficiency, generalization, and often results in better performance by allowing tasks to inform and constrain each other's learning.
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