Extending Semi-supervised Learning Methods for Inductive Transfer Learning

Abstract

Inductive transfer learning and semi-supervised learning are two different branches of machine learning. The former tries to reuse knowledge in labeled out-of-domain instances while the later attempts to exploit the usefulness of unlabeled in-domain instances. In this paper, we bridge the two branches by pointing out that many semi-supervised learning methods can be extended for inductive transfer learning, if the step of labeling an unlabeled instance is replaced by re-weighting a diff-distribution instance. Based on this recognition, we develop a new transfer learning method, namely COITL, by extending the co-training method in semi-supervised learning. Experimental results reveal that COITL can achieve significantly higher generalization and robustness, compared with two state-of-the-art methods in inductive transfer learning.