Transfer learning is a machine learning technique where knowledge gained from one task is applied to another related task. It involves using a pretrained model, trained on a large dataset, as a starting point for a new task. The pretrained model's learned representations and features are transferred to the new model, which is then fine-tuned or used as a feature extractor. This approach helps overcome data limitations and reduces the computational burden of training from scratch. Transfer learning allows models to leverage pre-existing knowledge and generalize well, leading to improved performance and faster convergence on new tasks.

How does transfer learning work?

Transfer learning works by leveraging knowledge gained from a pretrained model. The pretrained model's learned representations and features are utilized as a starting point for a new task. The pretrained model is either fine-tuned by updating its weights on the new task-specific data or used as a feature extractor, where its intermediate layers' outputs serve as inputs for a new model. This allows the model to benefit from the pre-existing knowledge and generalize well on the new task, even with limited data or computational resources.

Why does transfer learning work?

Transfer learning works because pretrained models capture generalizable features from large datasets, which can be effectively leveraged and adapted to new tasks, leading to improved performance with limited data.