Transfer learning is a sophisticated technique in the realm of machine learning. It involves leveraging the knowledge obtained from solving one problem and applying it to another, distinct yet related problem. In the context of transfer learning, a pre-trained model serves as a foundation. This model is initially trained on a large-scale dataset to identify patterns within the data. Subsequently, it can be fine-tuned using a smaller, relevant dataset to carry out a specific task. By capitalizing on the knowledge amassed during the pre-training stage, transfer learning not only minimizes the volume of data needed for model training but also enhances the model's performance.
Transfer learning proves especially valuable in fields where an abundance of labeled data is hard to come by, like medical imaging or natural language processing. It has been effectively implemented in numerous applications, including image classification, text classification, and speech recognition.