Transfer learning

Transfer learning (also known as inductive transfer) is a machine learning method that focuses on the reuse of knowledge acquired for a different but related task. Transfer learning is used when there is a limited amount of data available and the goal is to learn from a source domain while the target domain is already known. There are two main types of transfer learning, inductive transfer and transductive transfer.

Inductive transfer is when the source and target are distinct problems but use the same features/inputs, and is used when only limited source data is available. Inductive transfer is typically used in supervised learning settings, where the goal is to apply knowledge from a source domain to a target domain.

Transductive transfer is when the source and target domains have similar but not identical inputs, and is used when some source data is available but not enough for a full training set. In this case, the source and target models learn from multiple sources, such as expert knowledge or natural language.

Research into transfer learning began in the mid-1990s with work looking at ways of transferring knowledge from input sources. Another common approach to transfer learning is to pre-train a neural network on large datasets and then fine-tune the network on smaller datasets. The goal is to increase generalization and accuracy without collecting large amounts of data, or to repurpose a large pre-trained model for a new task.

Transfer learning has become increasingly important in the field of computer vision, natural language processing, and other machine learning applications due to the presence of large datasets. Increasingly, researchers are looking at how to apply transfer learning to more complex problems such as robotics and artificial intelligence.

Transfer learning is a crucial technique for achieving artificial intelligence and is a key area of research in the field. With the increasing demand for datasets and computational power, transfer learning will continue to be an important area of research as researchers seek to increase accuracy and reduce the cost of data collection.