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. 2022 Dec 12:1–23. Online ahead of print. doi: 10.1007/s11280-022-01119-x

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 7 — Testing accuracy of SVHN dataset in cross-model hybrid scenario. Subfigure (a) and (b) come from the same batch of experiments. I.e., in the experiment where ten models contain 1 ResNet34 and 9 DenseNet121 models. The inference accuracy of num_ResNet34 = 1 in subfigure (a) and num_DenseNet121= 9 in (b) show the average performance of the ResNet34 and DenseNet121 models, respectively