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Learning by Transferring from Unsupervised Universal Sources

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

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Abstract

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
@conference{Wang-2016-5485,
title = {Learning by Transferring from Unsupervised Universal Sources},
author = {Yuxiong Wang and Martial Hebert},
booktitle = {Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)},
month = {February},
year = {2016},
}
2017-09-13T10:38:30+00:00