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Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

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Abstract

One of the most prominent problems in machine learning in the age of deep learning is the availability of sufficiently large annotated datasets. For specific domains, e.g. animal species, a long-tail distribution means that some classes are observed and annotated insufficiently. Additional labels can be prohibitively expensive, e.g. because domain experts need to be involved. However, there is more information available that is to the best of our knowledge not exploited accordingly.

In this paper, we propose to make use of preexisting class hierarchies like WordNet to integrate additional domain knowledge into classification. We encode the properties of such a class hierarchy into a probabilistic model. From there, we derive a novel label encoding and a corresponding loss function. On the ImageNet and NABirds datasets our method offers a relative improvement of \(10.4\%\) and \(9.6\%\) in accuracy over the baseline respectively. After less than a third of training time, it is already able to match the baseline’s fine-grained recognition performance. Both results show that our suggested method is efficient and effective.

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Notes

  1. 1.

    https://species.wikimedia.org/wiki/Main_Page.

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Author information

Authors and Affiliations

  1. Computer Vision Group, Friedrich Schiller University Jena, Jena, Germany

    Clemens-Alexander Brust & Joachim Denzler

  2. Michael Stifel Center Jena, Jena, Germany

    Joachim Denzler

Authors
  1. Clemens-Alexander Brust
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  2. Joachim Denzler
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Corresponding author

Correspondence to Clemens-Alexander Brust .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Brust, CA., Denzler, J. (2020). Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_1

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  • Online ISBN: 978-3-030-41404-7

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