Transfer learning is a technique within machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. This helps to shorten the training time needed for a large set of data and prevents the need to start from scratch. Transfer learning has many practical applications in fields such as computer vision, natural language processing, and speech recognition.
Transfer learning works by taking a network that is already trained on a certain task, and reusing its weights on another related task. This allows for faster and more accurate completion of the task without the need to start from a blank slate. It should be noted that transfer learning is different from domain adaptation, which applies only to the source domain while transfer learning can be applied to both the source and target domains.
One of the major advantages of transfer learning is its ability to reduce training time. It requires fewer labeled training samples for the target task compared to training from scratch, which can be an expensive, time-consuming process. A model trained from scratch would also require more parameters to learn, leading to a much higher chance of overfitting.
A common example of transfer learning in practice is with convolutional neural networks (CNNs). CNNs are a type of neural network that primarily deals with images. Pre-trained CNNs, containing weights from networks trained on large datasets, such as ImageNet, can be used to recognize objects in images. As the CNNs are already trained on a large number of samples, other computer vision tasks can be completed easily with a far smaller dataset.
In conclusion, transfer learning is a powerful technique that enables machines to quickly learn new tasks using pre-trained networks. By leveraging the knowledge gained while solving one problem, it reduces the training time and parameters needed for the new task. This makes transfer learning an invaluable solution to various problems in Computer vision, NLP and Speech recognition.
