What is Transfer Learning?

Transfer Learning is a technique where a pre-trained model is applied to a new problem.

Transfer learning is particularly useful in situations where data is scarce or when there is limited time for training, as it enhances performance more quickly and efficiently than training a new model from scratch.

Transfer Learning involves using knowledge gained from a pre-trained model to improve performance on a different but related task, whereas Fine-tuning is the process of adapting a pre-trained model to a specific task with some modifications.

Suppose there is a person who learned to play the piano as a child. Later in life, this person decides to learn a new instrument, the guitar.

Though piano and guitar are different, the music theory and sense of rhythm learned from playing the piano significantly aid in learning the guitar.

In other words, the previous experience of learning piano can help this person learn the guitar more quickly and easily.

Similarly, transfer learning involves applying previously learned knowledge to enhance learning efficiency with new problems.

It does not require a large amount of data for new problems. By leveraging the knowledge of a pre-trained model, high performance can be achieved with a smaller dataset.

Since it utilizes the features of a pre-trained model, it can complete learning much faster compared to training a model from scratch.

Particularly when the new dataset is similar to the existing dataset, transfer learning can achieve very high performance.