Transfer learning is a technique used in machine learning where a model trained on one task is used to improve the performance of a model on a different, but related, task. In other words, the knowledge learned by a model on one task can be used to aid the model in learning a new task. It is analogous to a person using their experience and knowledge gained from one situation to help them in a new, but related, situation.
In the context of deep learning, transfer learning can be leveraged by using pre-trained models as a starting point for a new model. For example, if we want to train a model to classify images of cars, we can use a pre-trained model which has already learned to recognize objects in images, such as VGG or ResNet. We can then remove the outputs from the pre-trained model and replace them with our own outputs, which will correspond to the classes of cars we want to classify.
By using a pre-trained model as a starting point, we are able to leverage the large amounts of labeled data used to train the original model, which we may not have access to for our new task. This can also significantly reduce the amount of training data required for our new task, as the pre-trained model has already learned a rich set of features that are likely to be relevant to our new task. Furthermore, the low-level features learned by the pre-trained network can act as a form of feature extraction that can be used to aid in our new task.
Another key benefit of transfer learning is that it can greatly reduce the computational resources required to train a deep learning model from scratch, as the pre-trained model has already learned many of the complex representations required for our new task.
In summary, transfer learning is a powerful technique that can greatly improve the performance of deep learning models on new, related tasks. By leveraging the knowledge learned by pre-trained models, we can reduce the amount of training data required and greatly reduce the computational resources required for training.