Transfer learning is a technique in which a pre-trained neural network, trained on a large and general dataset, is re-purposed for a specific task. By leveraging the knowledge acquired during the pre-training phase, transfer learning allows the network to achieve better performance with less training data compared to training from scratch. It is commonly used in domains where labeled data is scarce.