Transfer learning is the practice of utilizing knowledge gained from solving one problem to improve the performance of a model on a different but related problem. In machine learning, transfer learning involves taking a pre-trained model, which has already been trained on a large dataset, and using it as a starting point for a new model.
In deep learning, transfer learning allows developers to take advantage of the knowledge gained by training models on large datasets. This approach can save significant amounts of time and computational resources by avoiding the need to train a model from scratch on a new dataset.
Transfer learning can be utilized in deep learning models in several ways. One popular method is fine-tuning, where the weights of a pre-trained model are adjusted slightly to better suit the new problem. Another method is feature extraction, where the pre-trained model is used as a feature extractor, and only the final layer is replaced with a new layer customized for the new task.
Fine-tuning involves retraining the pre-trained model with a new dataset or a set of new classes. The weights of the pre-trained model are initialized with the knowledge learned from the previous training task, and then tuned on the new dataset to improve performance. For example, a pre-trained model that is good at detecting images of cars, can be fine-tuned to detect images of trucks or buses.
Feature extraction is another way of using pre-trained models. Here, the pre-trained model is used as a feature extractor. We take the output of some of the pre-trained layers and use these as inputs to a new custom classifier. For example, in image classification, the convolutional base of a pre-trained model can be used to extract image features, and these features can then be used as input to a classifier that is trained on a new set of image classes.
In summary, transfer learning is a technique that can be used to leverage pre-trained models to improve the performance of deep learning models. It can save time and computational resources while improving the quality of the predictions.