Guest editor’s introduction: special issue on inductive transfer learning

Abstract

Inductive transfer or transfer learning refers to the problem of retaining and applying the knowledge learned in one or more tasks to develop efficiently an effective hypothesis for a new task. While all learning involves generalization across problem instances, transfer learning emphasizes the transfer of knowledge across domains, tasks, and distributions that are similar but not the same. For example, learning to recognize chairs might help to recognize tables; or learning to play checkers might improve the learning of chess. While people are adept at inductive transfer, even across widely disparate domains, we have only begun to develop associated computational learning theory and there are few machine learning systems that exhibit knowledge transfer. Inductive transfer invokes some of the most important questions in artificial intelligence. Amongst its challenges are questions such as: • What is the best representation and method for retaining learned background knowledge? How does one index into such knowledge? • What is the best representation and method for transferring prior knowledge to a new task? • How does the use of prior knowledge affect hypothesis search heuristics? • What is the nature of similarity or relatedness between tasks for the purposes of learning? Can it be measured? • What role does curriculum play in the sequential learning of tasks?