Transfer Learning

Transfer learning leverages knowledge gained while solving one problem and applies it to a different but related problem. This approach is particularly useful when there is limited labeled data for the target task. By fine-tuning a pre-trained model on a new dataset, it can achieve high performance with less training time and resources. Transfer learning is widely used in natural language processing and computer vision, where large-scale models can be adapted to specific tasks.