Multitask learning

Multitask learning is an artificial intelligence (AI) subfield of machine learning. It seeks to have a machine learn a single model from multiple tasks with the goal of improving generalization accuracy across all tasks. It is a supervised machine learning technique in which the model learns to complete multiple tasks simultaneously by training on multiple tasks sharing a common input.

The original motivation for multitask learning was to allow machine learning models to better use the knowledge from related tasks. By sharing parameters across tasks, the model can improve its performance on a specific task even if data is limited, so that it does not require as large a dataset as it would normally do if it was only trained on the task.

Multitask learning can be divided into two basic task types: hard multitasking and soft multitasking. Hard multitasking refers to training of a single model to perform multiple tasks on the same input data. Soft multitasking refers to training multiple models to perform different tasks on the same input data. Soft multitasking is more suitable when tasks are very different.

The effectiveness of multitask learning in achieving better performance has been demonstrated with many successful applications in computer vision, natural language processing, robotics, and many other areas. The challenge with multitask learning is to identify the right model architecture and optimization settings, which can be a difficult task, especially for complex multitask problems.

In recent years, there has been an increasing research interest in multitask learning. This is due to its potential to improve performance in many machine learning tasks, and its potential to reduce dataset size requirements, allowing for easier training and deployment.