Transfer learning is an optimization that allows rapid progress or improved performance when modeling a second related task,Transfer learning expands our prediction ability. The idea of successful transfer is based on powerful molecule representation. Therefore, the small sample obstacle is not as frightened as before.
“Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better” --Rich Caruana