Transfer Learning

Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task.

It is used when there is a need to leverage knowledge from a related task to improve learning efficiency and performance on a new task. Transfer learning is commonly applied in scenarios where data for the new task is limited or expensive to obtain. The technique works by taking a pre-trained model, often trained on a large dataset, and adapting it to the new task through downstream finetuning or additional training.

For example, a model trained on a large dataset of general images can be finetuned to recognize specific objects in medical images. Another example is using a model trained on a large corpus of emails to improve performance on a specific text classification task, such as spam detection.

Transfer learning is important because it allows for faster training times, improved performance, and reduced data requirements. It is a powerful approach in machine learning, enabling the reuse of existing models and knowledge to tackle new challenges effectively.