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International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 403–413Cite as

Data Correction by a Generative Model with an Encoder and its Application to Structure Design

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

Abstract

An alternative training model is proposed for adversarial networks to correct a slightly defective data. Generator is first acquired by classical Generative Adversarial Networks, where the discriminator is trained only by feasible data. Then, both an encoder as the inverse mapping of the generator and a classifier which judges a feasibility of a generated data, are trained to lead the generator to correct an infeasible data by the minimum modification. The proposed method is applied to a housing member placement problem to satisfy every constraint for earthquake resistance, and evaluated by a rigorous structural calculation.

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan

    Takaya Ueda, Masataka Seo & Ikuko Nishikawa

Authors
  1. Takaya Ueda
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  2. Masataka Seo
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  3. Ikuko Nishikawa
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Corresponding author

Correspondence to Ikuko Nishikawa .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Cite this paper

Ueda, T., Seo, M., Nishikawa, I. (2018). Data Correction by a Generative Model with an Encoder and its Application to Structure Design. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_40

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  • DOI: https://doi.org/10.1007/978-3-030-01424-7_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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