Abstract
The paper investigates the algorithmic stability of learning a deep neural network in problems of recognition of the materials microstructure. It is shown that at 8% of quantitative deviation in the basic test set the algorithm trained network loses stability. This means that with such a quantitative or qualitative deviation in the training or test sets, the results obtained with such trained network can hardly be trusted.
Although the results of this study are applicable to the particular case, i.e. problems of recognition of the microstructure using ResNet-152, the authors propose a cheaper method for studying stability based on the analysis of the test, rather than the training set.
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Acknowledgements
The reported study was funded by the Ministry of Science and Higher Education of the Russian Federation (the unique identifier RFMEFI58617X0055) and by the EC Horizon 2020 is MSCA-RISE-2016 FRAMED Fracture across Scales and Materials, Processes and Disciplines. The authors are grateful to the staff of the Institute of Nanosteels of MSTU named after G.I. Nosov, in particular to M. P. Baryshnikov, for the experimental data provided, which made it possible to train the constructed neural network with a given accuracy.
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Klyuev, A.V., Stolbov, V.Y., Gitman, M.B., Klestov, R.A. (2020). Stability of the Deep Neural Networks Learning Process in the Recognition Problems of the Material Microstructure. In: Antipova, T. (eds) Integrated Science in Digital Age. ICIS 2019. Lecture Notes in Networks and Systems, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-22493-6_27
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DOI: https://doi.org/10.1007/978-3-030-22493-6_27
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