Multi-Task Learning

Instead of training separate models for each task, MTL trains one model to handle multiple tasks simultaneously. The idea is that by learning related tasks together, the model can leverage shared knowledge and patterns between them. This can lead to better performance on each individual task compared to training them separately, especially when data for some tasks is limited.

Think of it like a student studying for several exams. If the exams cover related subjects, learning them together might help the student understand the core concepts more deeply, benefiting all their scores. In AI, this pooling of information often helps the model generalize better and sometimes even learn tasks that would be difficult to learn in isolation. It's commonly used in areas like natural language processing, computer vision, and recommendation systems, where models often need to perform a variety of related functions.