Transfer learning is a machine learning technique that leverages knowledge gained from one task to enhance the performance of a model on a different, yet related task. This approach is particularly valuable in deep learning, where training models from scratch can be resource-intensive. By utilizing pre-trained models, transfer learning allows for quicker and more efficient training, especially in fields like computer vision and natural language processing. It narrows the model bias by applying insights from a source task to improve learning outcomes in a target task, ultimately leading to better performance with less data and computational effort ¹².