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. 2017 Dec 5;7:16991. doi: 10.1038/s41598-017-17299-w

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© The Author(s) 2017

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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(a) Classification accuracies for the rotation invariance of the R3DVS descriptors. The horizontal axis indicates number of the rotated replicas that are fed into the CNNs. The vertical axis indicates classification accuracy averaged over 20 test results for randomly-chosen 50 samples of the oxides. (b) and (c) are t-SNE feature visualisations projected from the last Leaky ReLU layers embedded in the convolutional layers with 10 and 30 rotated replicas, respectively. The types of the marks indicate the 50 targets of the oxides to be classified.