What are the key benefits of using transfer learning in machine learning?

Transfer learning allows a model pre-trained on a large dataset to be fine-tuned on a smaller, related dataset. The key benefits include reduced training time, as the model starts with learned features, improved performance on small datasets due to leveraging knowledge from the larger dataset, and decreased computational costs. It is particularly beneficial in domains like image recognition and natural language processing, where labeled data can be scarce.