Why is Transfer Learning useful?

Example Answer

Transfer learning is useful because many target tasks do not have enough labeled data or budget to train a high-quality model from scratch. Starting from a pretrained model gives you a strong representation base and usually leads to faster convergence and better performance.

It is especially valuable when the source and target tasks share meaningful structure, such as images, language, or speech. In practice, it has become one of the most effective ways to make strong models practical outside very large research environments.

Common Poor Answer

A weak answer says transfer learning saves time, but never explains why pretrained representations help when target data is limited.