What does the term "transfer learning" refer to in the context of AI?

Transfer learning refers to the process of taking a model that has been previously trained on a certain task or domain and reusing it as the starting point for a new task or domain. This approach is particularly useful when you're dealing with limited data for the new task, as the model has already learned valuable features from the original data it's been trained on. This not only saves time and computational resources, but it also enables the model to achieve better performance due to the foundational knowledge it has acquired.

For instance, a neural network trained on a large image dataset can be fine-tuned for a specific application, like identifying medical images. The underlying representations learned from the broad dataset can assist in enhancing the model’s performance in the more specialized area, thus making transfer learning a valuable technique in machine learning and AI applications.