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Authors: Jeroen Manders 1 ; Twan van Laarhoven 2 and Elena Marchiori 3

Affiliations: 1 Institute for Computing and Information Science, Radboud University, Nijmegen, The Netherlands, TNO and The Netherlands ; 2 Institute for Computing and Information Science, Radboud University, Nijmegen, The Netherlands, Faculty of Management, Science and Technology, Open University, Heerlen and The Netherlands ; 3 Institute for Computing and Information Science, Radboud University, Nijmegen and The Netherlands

ISBN: 978-989-758-351-3

Keyword(s): Adversarial Learning, Meta-learning.

Abstract: We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level. We tackle this problem by introducing a method based on adversarial learning which forces the label uncertainty predictions on the target domain to be indistinguishable from those on the source domain. Pre-trained deep neural networks are used to generate deep features having high transferability across related domains. We perform an extensive experimental analysis of the proposed method over a wide set of publicly available pre-trained deep neural networks. Results of our experiments on domain adaptation tasks for image classification show that class prediction uncertainty alignme nt with features extracted from pre-trained deep neural networks provides an efficient, robust and effective method for domain adaptation. (More)

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Paper citation in several formats:
Manders, J.; van Laarhoven, T. and Marchiori, E. (2019). Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 221-231. DOI: 10.5220/0007519602210231

@conference{icpram19,
author={Jeroen Manders. and Twan van Laarhoven. and Elena Marchiori.},
title={Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={221-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007519602210231},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
SN - 978-989-758-351-3
AU - Manders, J.
AU - van Laarhoven, T.
AU - Marchiori, E.
PY - 2019
SP - 221
EP - 231
DO - 10.5220/0007519602210231

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