Transfer learning is a technique in deep learning where a pre-trained model is reused on a new problem. It is popular in deep learning because it can train deep neural networks with comparatively little data. Instead of starting the learning process from scratch, transfer learning tries to exploit what has been learned in one task to improve generalization in another. The weights that a network has learned at "task A" are transferred to a new "task B". The general idea is to use the knowledge a model has learned from a task with a lot of available labeled training data in a new task that doesnt have much data.
Transfer learning is not a machine learning technique, but can be seen as a "design methodology" within the field. It is also not an exclusive part or study-area of machine learning. Nevertheless, it has become quite popular in combination with neural networks that require huge amounts of data and computational power.
Transfer learning can be applied to various tasks, such as image classification and natural language processing. It is particularly useful when there is a small training dataset, as the weights from the pre-trained models can be used to initialize the weights of the new model. The advantage of pre-trained models is that they are generic enough for use in other real-world applications.
Some key benefits of transfer learning include:
- Improved performance: Transfer learning can help achieve significantly higher performance than training with only a small amount of data.
- Computational efficiency: Transfer learning is computationally efficient and helps achieve better results using a small data set.
- Leveraging pre-trained models: Transfer learning leverages feature representations from a pre-trained model, so a new model doesnt have to be trained from scratch.
Overall, transfer learning is a powerful technique that can help improve the performance of deep learning models, especially when there is limited data available.
