Transfer learning is a technique where a pre-trained model’s knowledge is leveraged to enhance the performance of a related but different task. Instead of training a model from scratch for a specific task, transfer learning involves using a model that has been trained on a large dataset for a general task and fine-tuning it for the specific task at hand. This approach capitalizes on the knowledge the model has gained from its initial training, enabling faster convergence and improved results with smaller amounts of task-specific data.
Transfer learning works because many features learned by a model during its initial training are applicable to other tasks. The early layers of deep neural networks, for example, often learn basic features like edges and textures that are useful for various image-related tasks. By retaining these learned features and adapting them to the new task, transfer learning reduces the need for extensive new training and data collection efforts.
« Back to Glossary Index
