Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks/domains to improve generalization in the tasks/domains of interest.
Transfer learning (or knowledge transfer) is a strategy to address the variation in the data distributions within heterogeneous datasets by reutilizing knowledge from source problems to solve target tasks. This strategy, inspired by psychology[1], aims to exploit common features between related tasks and domains. For instance, an expert in magnetic resonance imaging (MRI) can specialize in computed tomography (CT) imaging faster than someone with no knowledge in either MRI or CT.
According to Pan and Yang[2], a domain in transfer learning can be defined as
Transfer learning approaches can be categorized based on the availability of labels in source and/or target domains during the optimization[2]: unsupervised (unlabeled data), transductive (labels available only in the source domain), and inductive (labels available in the target domains and, optionally, in the source domains).