Transitive Transfer Learning

Abstract

Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related. If there is little overlap between the two domains, performing knowledge transfer between these domains will not be effective. Inspired by human transitive inference and learning ability, whereby two seemingly unrelated concepts can be connected by a string of intermediate bridges using auxiliary concepts, in this paper we study a novel learning problem: Transitive Transfer Learning (abbreviated to TTL). TTL is aimed at breaking the large domain distances and transfer knowledge even when the source and target domains share few factors directly. For example, when the source and target domains are documents and images respectively, TTL could use some annotated images as the intermediate domain to bridge them. To solve the TTL problem, we propose a learning framework to mimic the human learning process. The framework is composed of an intermediate domain selection component and a knowledge transfer component. Extensive empirical evidence shows that the framework yields state-of-the-art classification accuracies on several classification data sets.