What is Transfer Learning in machine learning?

Transfer Learning is a technique used in machine learning where a model that has been pre-trained on a large dataset is adapted to perform well on a new, often smaller dataset or a different but related task. The core idea is to leverage the knowledge gained by the model during its initial training phase, which typically involves extracting features or patterns that can be useful for solving the new problem at hand.

By utilizing a pre-trained model, practitioners can significantly reduce the time and computational resources required to train a model from scratch. This is especially beneficial in scenarios where there is limited data for the new task, as the pre-trained model can retain useful representations learned from the large dataset it was originally trained on. Consequently, this approach often leads to improved performance and generalization on the new task since the model is building upon existing knowledge.

In contrast, training models from scratch entails starting the learning process with no pre-existing knowledge, which can be resource-intensive and less efficient. Eliminating all prior knowledge from a model runs counter to the concept of transfer learning, which actively aims to retain and adapt learned information. Finally, while data storage strategies might play a role in machine learning workflows, they are not relevant to the concept of transfer learning.