What are the benefits of using transfer learning in deep learning applications?

Transfer learning offers several benefits in deep learning applications: it reduces training time significantly since the model can leverage pre-trained weights from a related task, which can improve performance when data is scarce. It allows for better generalization, as the model can learn features from a larger dataset and apply them to a specific task. Additionally, it can facilitate the development of models for specialized tasks where training from scratch would require significant resources.