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Towards More Reliable Transfer Learning

  • Zirui WangEmail author
  • Jaime Carbonell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

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. Code related to this paper is available at: https://github.com/iedwardwangi/ReliableMSTL.

Supplementary material

478890_1_En_47_MOESM1_ESM.pdf (180 kb)
Supplementary material 1 (pdf 180 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Language Technologies InstituteCarnegie Mellon UniversityPittsburghUSA

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