Accelerating Active Learning with Transfer Learning

Active learning, transfer learning, and related techniques are unified by a core theme: efficient and effective use of available data. Active learning offers scalable solutions for building effective supervised learning models while minimizing annotation effort. Transfer learning utilizes existing labeled data from one task to help learn related tasks for which limited labeled data are available. There has been limited research, however, on how to combine these two techniques. In this paper, we present a simple and principled transfer active learning framework that leverages pre-existing labeled data from related tasks to improve the performance of an active learner. We derive an intuitive bound on the generalization error for the classifiers learned by this algorithm that provides insight into the algorithm's behavior and the problem in general. We provide experimental results using several well-known transfer learning data sets that confirm our theoretical analysis. What is more, our results suggest that this approach represents a promising solution to a specific weakness of active learning algorithms: cold starts with zero labeled data.