Introduction to Multi-Task Learning:
Multi-Task Learning (MTL) is a type of machine learning technique where a model is trained to perform multiple tasks simultaneously. In deep learning, MTL refers to training a neural network to perform multiple tasks by sharing some of the network’s layers and parameters across tasks.In MTL, the goal is to improve the generalization performance of the model by leveraging the information shared across tasks. By sharing some of the network’s parameters, the model can learn a more efficient and compact representation of the data, which can be beneficial when the tasks are related or have some commonalities.