Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although practical ties between the two fields are limited. From the practical standpoint, reusing or transferring information from previously learned tasks for the learning of new tasks has the potential to significantly improve the sample efficiency of a reinforcement learning agent. History In 1976, Stevo Bozinovski and Ante Fulgosi published a paper explicitly addressing transfer learning in neural networks training. The paper gives a mathematical and geometrical model of transfer learning. In 1981, a report was given on the application of transfer learning in training a neural network on a dataset of images representing letters of computer terminals. Both positive and negative transfer learning was experimentally demonstrated. In 1993, Lorien Pratt published a paper on transfer in machine learning, formulating the discriminability-based transfer (DBT) algorithm. In 1997, Pratt and Sebastian Thrun guest edited a special issue of Machine Learning devoted to transfer learning, and by 1998, the field had advanced to include multi-task learning, along with a more formal analysis of its theoretical foundations. Learning to Learn, edited by Thrun and Pratt, is a 1998 review of the subject. Transfer learning has also been applied in cognitive science, with Pratt also guest editing an issue of Connection Science on reuse of neural networks through transfer in 1996. Andrew Ng said in his NIPS 2016 tutorial that TL will be the next driver of ML commercial success after supervised learning to highlight the importance of TL. In the 2020 paper "Rethinking Pre-Training and self-training", Zoph et al. show that pre-training can hurt accuracy, and advocate self-training instead. Definition The definition of transfer learning is given in terms of domains and tasks. A domain consists of: a feature space and a marginal probability distribution , where . Given a specific domain, , a task consists of two components: a label space and an objective predictive function . The function is used to predict the corresponding label of a new instance . This task, denoted by , is learned from the training data consisting of pairs , where