In machine learning, transfer learning is a technique that can be used to imrove the performance of a model on a task by reusing knowledge from a related task that has already been learned. For example, a model that has been trained on a large dataset of images of faces may be able to learn to recognize new faces more quickly and accurately than a model that has been trained from scratch on a smaller dataset. Transfer learning can be used in a variety of different ways, including fine-tuning a pre-trained model, using a pre-trained model as a feature extractor, or using a pre-trained model to initialize a new model.
Some of the emerging trends in transfer learning include the use of deep learning for cross-domain adaptation, the use of transfer learning for few-shot learning, and the use of transfer learning for unsupervised domain adaptation.
