/Learning by Transferring from Unsupervised Universal Sources

Learning by Transferring from Unsupervised Universal Sources

Yuxiong Wang and Martial Hebert
Conference Paper, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February, 2016

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


Category classifiers trained from a large corpus of annotated data are widely accepted as the sources for (hypothesis) transfer learning. Sources generated in this way are tied to a particular set of categories, limiting their transferability across a wide spectrum of target categories. In this paper, we address this largely-overlooked yet fundamental source problem by both introducing a systematic scheme for generating universal source hypotheses and proposing a principled, scalable approach to automatically tuning the transfer process. Our approach is based on the insights that expressive source hypotheses could be generated without any supervision and that a sparse combination of such hypotheses facilitates recognition of novel categories from few samples. We demonstrate improvements over the state-of-the-art on object and scene classification in the small sample size regime.

BibTeX Reference
author = {Yuxiong Wang and Martial Hebert},
title = {Learning by Transferring from Unsupervised Universal Sources},
booktitle = {Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)},
year = {2016},
month = {February},