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Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance

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Software Engineering Perspectives in Intelligent Systems (CoMeSySo 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1294))

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Abstract

The article examines the problem of quality assessment for generative adversarial networks (GANs). There is no unified and universal metric to compare and evaluate GAN. Well-known approaches for the GAN quality assessment are focused on images generating neural networks. This paper considers the problem of the quality determination of arbitrary GAN operating with various data sets. For problem solution, a quality assessment method of arbitrary GAN is proposed, which differs by the modification of the calculation formulas Inception Score and Fréchet Inception Distance. The included changes allow the use of these metrics to assess and compare arbitrary GANs. The developed method was tested during experiments on the objects generation from marked (MNIST) and unmarked (Human Activity Recognition Using Smartphones and Epileptic Seizure Recognition) datasets. The obtained results confirm the possibility of applying the modified metrics Inception Score and Fréchet Inception Distance to assess the quality of arbitrary GANs.

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Authors and Affiliations

  1. Tambov State Technical University, 392000, Tambov, Russian Federation

    Artem Obukhov & Mikhail Krasnyanskiy

Authors
  1. Artem Obukhov
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  2. Mikhail Krasnyanskiy
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Correspondence to Artem Obukhov .

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Editors and Affiliations

  1. Faculty of Applied Informatic, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlín, Czech Republic

    Zdenka Prokopova

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Obukhov, A., Krasnyanskiy, M. (2020). Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-63322-6_8

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