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.
Difference Between Transfer Learning and Fine-tuning
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.
Understanding Transfer Learning Through Analogy
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.
Advantages of Transfer Learning
1. Data Efficiency
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.
2. Reduced Training Time
Since it utilizes the features of a pre-trained model, it can complete learning much faster compared to training a model from scratch.
3. Enhanced Performance
Particularly when the new dataset is similar to the existing dataset, transfer learning can achieve very high performance.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.