Transfer learning is a machine learning technique where a model trained on one task is used to solve a different but related task. This approach is particularly useful when limited labeled data is available for the target task, as the knowledge acquired from the source task can be leveraged to improve performance on the target task. Transfer learning has been successfully applied in a variety of fields, including computer vision, natural language processing, and speech recognition. It allows for faster and more efficient learning on new tasks by reusing information acquired from previously learned tasks.