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. 2019 Nov 11;9:16526. doi: 10.1038/s41598-019-52937-5

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The resistance of different DNN architectures against missing values and noise. (a) In each plot, the x-axis shows the fragment of randomly-selected input values that are set to zero (dropout), and y-axis shows either regression MSE or classification accuracy for the test dataset. (b) The x-axis shows the standard error (SD) of a zero-centered Gaussian noise which was added to the input values, and y-axis shows MSE or accuracy for the test dataset. Colors indicate different DNN architectures (see the figure legend).