How does transfer learning enable the reuse of pre-trained models in new tasks, and what are its benefits?

Transfer learning is a machine learning technique where knowledge gained from solving one task is applied to a related task. Pre-trained models are fine-tuned on new data to adapt to the specific characteristics of the new task. Transfer learning accelerates model training, requires less labeled data, and improves performance on target tasks, especially when training data is limited.