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Authors: Hlynur Davíð Hlynsson ; Alberto N. Escalante-B. and Laurenz Wiskott

Affiliation: Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum and Germany

Keyword(s): Data Efficiency, Deep Learning, Neural Networks, Slow Feature Analysis, Transfer Learning.

Abstract: In this paper, we propose a new experimental protocol and use it to benchmark the data efficiency — performance as a function of training set size — of two deep learning algorithms, convolutional neural networks (CNNs) and hierarchical information-preserving graph-based slow feature analysis (HiGSFA), for tasks in classification and transfer learning scenarios. The algorithms are trained on different-sized subsets of the MNIST and Omniglot data sets. HiGSFA outperforms standard CNN networks when the models are trained on 50 and 200 samples per class for MNIST classification. In other cases, the CNNs perform better. The results suggest that there are cases where greedy, locally optimal bottom-up learning is equally or more powerful than global gradient-based learning.

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Paper citation in several formats:
Hlynsson, H. ; Escalante-B., A. and Wiskott, L. (2019). Measuring the Data Efficiency of Deep Learning Methods. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 691-698. DOI: 10.5220/0007456306910698

@conference{icpram19,
author={Hlynur Davíð Hlynsson and Alberto N. Escalante{-}B. and Laurenz Wiskott},
title={Measuring the Data Efficiency of Deep Learning Methods},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={691-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007456306910698},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Measuring the Data Efficiency of Deep Learning Methods
SN - 978-989-758-351-3
IS - 2184-4313
AU - Hlynsson, H.
AU - Escalante-B., A.
AU - Wiskott, L.
PY - 2019
SP - 691
EP - 698
DO - 10.5220/0007456306910698
PB - SciTePress

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