What is Transfer Learning?

Instruction: Explain the concept of Transfer Learning and how it differs from traditional machine learning models.

Context: This question assesses the candidate's basic understanding of Transfer Learning, its importance, and how it is differentiated from the conventional machine learning approach.

Updated

Example Answer

Transfer learning is the practice of taking knowledge learned on one task or dataset and reusing it to improve performance on a related target task. Instead of training from scratch, you start from a model that already knows something useful about the input domain.

In modern ML, that often means using a pretrained backbone or language model and adapting it through fine-tuning, feature extraction, or lightweight task-specific layers. The main benefit is that it reduces the amount of target-task data and compute needed to get strong performance.

Common Poor Answer

A weak answer says transfer learning means reusing a model, without explaining that useful representations learned on a source task are adapted to a related target task.

Related Questions