Transfer Learning

Human learners appear to have inherent ways to transfer knowledge between tasks. That is, we recognize and apply relevant knowledge from previous learning experiences when we encounter new tasks. The more related a new task is to our previous experience, the more easily we can master it. Common machine learning algorithms, in contrast, traditionally address isolated tasks. Transfer learning attempts to improve on traditional machine learning by transferring knowledge learned in one or more source tasks and using it to improve learning in a related target task (see Figure 1). Techniques that enable knowledge transfer represent progress towards making machine learning as efficient as human learning. This chapter provides an introduction to the goals, settings, and challenges of transfer learning. It surveys current research in this area, giving an overview of the state of the art and outlining the open problems. ABStrAct