Multi-task Learning

Multi-task Learning (MTL) is a machine learning paradigm where a model is trained to perform multiple tasks simultaneously, leveraging shared representations and information across these tasks. This approach contrasts with single-task learning, where a model is trained on one specific task at a time. MTL can enhance performance by promoting generalization and improving efficiency, as the model learns from related tasks and can benefit from shared features, data, or underlying structures. It is particularly useful in scenarios where tasks are correlated or when data for individual tasks may be limited. MTL is commonly applied in various domains, including natural language processing, computer vision, and speech recognition, where tasks like classification, regression, and prediction can benefit from mutual reinforcement during training.