Question - When is multi-task learning usually preferred?
Answer -
Multi-task learning with deep neural networks is a subfield wherein several tasks are learned by a shared model. This reduces overfitting, enhances data efficiency, and speeds up the learning process with the use of auxiliary information. Multi-task learning is useful when there is a small amount of data for any given task and we can benefit from training a deep learning model on a large dataset.