What is the concept of transfer learning?

Transfer learning is a technique that allows a model trained on one task to adapt for another. This approach utilizes the knowledge gained from a previously trained model, often on a large dataset, to apply it to a different but related task, which may have limited data. The core idea is to leverage the features learned from the source task to improve the performance on the target task, thereby reducing the amount of training required and enhancing the efficiency of the learning process.

For example, if a model has been successfully trained to recognize objects in images, it can be fine-tuned with a smaller dataset to recognize different objects or perform another image-related task. This significantly speeds up the development of AI applications and allows practitioners to achieve better results with less resource investment. The effectiveness of transfer learning lies in its ability to generalize learned features and apply them across different domains.