Title: Towards more Reliable Transfer Learning,
Abstract: Multi-source transfer learning has been proven effective when within-target
labeled data is scarce. Previous work focuses primarily on exploiting domain
similarities and assumes that source domains are richly or at least comparably
labeled. While this strong assumption is never true in practice, this paper
relaxes it and addresses challenges related to sources with diverse labeling
volume and diverse reliability. The first challenge is combining domain
similarity and source reliability by proposing a new transfer learning method
that utilizes both source-target similarities and inter-source relationships.
The second challenge involves pool-based active learning where the oracle is
only available in source domains, resulting in an integrated active transfer
learning framework that incorporates distribution matching and uncertainty
sampling. Extensive experiments on synthetic and two real-world datasets
clearly demonstrate the superiority of our proposed methods over several
baselines including state-of-the-art transfer learning methods.