What Is Transfer Learning?

Transfer learning is a technique in computer vision where a new model is developed on the foundation of a pre-existing model to expedite the learning process with less data. This approach involves transferring the knowledge from a model trained on a large dataset to a new model tasked with learning a related but distinct problem, akin to leveraging skills from one domain to another, such as snowboarding skills aiding in learning to skateboard. Transfer learning is particularly beneficial when data is scarce, time is limited, or computational resources are constrained, as it reduces the need for extensive retraining from scratch. However, it may not be suitable when there is a significant mismatch between the domain of the pre-trained model and the new task, or when a large dataset is available, as it might introduce noise and reduce performance. Experiments demonstrate that the choice of an initial checkpoint can significantly influence the final model's quality, with better results stemming from more closely aligned starting points to the task at hand.