Transfer learning is a technique in machine learning where knowledge learned from one task is re-used to improve performance on a related task. It is a popular approach in deep learning, where pre-trained models can be reused as the starting point for a model on a second task. The general idea is to use the knowledge a model has learned from solving a related task with a lot of available labeled training data in a new task that doesnt have much data. Transfer learning is mostly used in computer vision and natural language processing tasks like sentiment analysis due to the huge amount of computational power required.
There are different ways to accomplish transfer learning, including finding a related learned task that has plenty of transferable labeled data, training the new model on that task, and then using it as a starting point for solving the initial task. Another way is to use a pre-trained model that has been trained using a large dataset to solve a similar task as the one at hand.
Transfer learning is not a machine learning technique per se, but rather a design methodology within the field. It is also not an exclusive part or study-area of machine learning. Instead, it is an optimization that allows rapid progress or improved performance when modeling the second task.
In summary, transfer learning is a technique in machine learning that allows the reuse of knowledge learned from one task to improve performance on a related task. It is a popular approach in deep learning, where pre-trained models can be reused as the starting point for a model on a second task.
