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. 2018 Feb 5;8:2369. doi: 10.1038/s41598-018-20521-y

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© The Author(s) 2018

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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An illustrative selection of deep learned binary patterns. Four randomly chosen patterns, for the same resolution of image, arising when M, the number of patterns, is reduced to 666 (a), and when M = 333 (b). These pattern formations suggest that when there are fewer patterns available, the algorithm focuses on low-resolution bases.