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dc.contributor.authorKohler, Michael
dc.contributor.authorKrzyżak, Adam
dc.contributor.authorWalter, Benjamin
dc.date.accessioned2023-05-24T15:07:48Z
dc.date.available2023-05-24T14:35:56Z
dc.date.available2023-05-24T15:07:48Z
dc.date.issued2023-05-24
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3850.2
dc.descriptionThis repository contains the Python code required to reproduce the simulation part of the paper "On the rate of convergence of image classifiers based on convolutional neural networks" from Kohler, Krzyżak, and Walter (2022) referenced below. The Python version used is Python 3.9.7. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 449102119. The Cifar-10 image dataset consisting of the real images from which the classes "cars" and "ships" were used can be downloaded from the link given below. In the Techincal Report "Learning Multiple Layers of Features from Tiny Images" from Alex Krizhevsky (2009) (for a link see below) this dataset of real images is described in more detail.de_DE
dc.language.isoende_DE
dc.relationCites;DOI;10.1007/s10463-022-00828-4
dc.relationReferences;URL;https://www.cs.toronto.edu/~kriz/cifar.html
dc.relationReferences;URL;https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
dc.rightsCreative Commons Attribution 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.classification312-01 Mathematikde_DE
dc.subject.ddc510
dc.titleOn the rate of convergence of image classifiers based on convolutional neural networks: Implementations of the estimates and links to image data setsde_DE
dc.typeSoftwarede_DE
tud.unitTUDa


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