Multi-Task Learning
Multi-Task Learning (MTL) is a machine learning paradigm where a model is trained to perform multiple related tasks simultaneously, sharing representations and parameters across tasks to improve generalization and efficiency. It leverages commonalities and differences between tasks to enhance learning performance, often leading to better accuracy and reduced overfitting compared to training separate models for each task. This approach is widely used in areas like natural language processing, computer vision, and recommendation systems.
Developers should use Multi-Task Learning when they have multiple related prediction problems that can benefit from shared knowledge, such as in joint sentiment analysis and topic classification in NLP, or object detection and segmentation in computer vision. It is particularly valuable in scenarios with limited labeled data per task, as it allows the model to learn more robust features by leveraging information from all tasks, improving overall performance and computational efficiency.