Multi-task learning (MTL) is a machine learning approach that focuses on learning multiple tasks simultaneously rather than training separate models for each task. This technique allows a single model to optimize multiple loss functions at once, leveraging shared information across tasks to improve overall performance. MTL is particularly effective when tasks are correlated, as it enables the model to learn generalized representations that are beneficial for all tasks involved ¹².

The motivation behind MTL is to create a “generalist” model that can handle various tasks by utilizing the relationships and commonalities between them. For instance, tasks that involve classifying images of animals may share features like fur patterns and colors, making them suitable for MTL ¹. However, it is essential to note that MTL may lead to negative transfer if the tasks are unrelated or if the information learned in one task contradicts another ¹³.

In practice, MTL often employs neural network architectures, particularly through hard parameter sharing, where different tasks utilize the same base representation of input data ¹⁴. This approach has gained popularity across various domains, including natural language processing, computer vision, and recommendation systems ¹⁵.