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Analysis of convolutional neural network image classifiers in a rotationally symmetric model: Implementations of the estimates and links to image data sets

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datacite.relation.cites https://doi.org/10.1109/TIT.2023.3262745
datacite.relation.references https://sites.google.com/a/lisa.iro.umontreal.ca/public_static_twiki/variations-on-the-mnist-digits
datacite.relation.references https://doi.org/10.1145/1273496.1273556
datacite.relation.references http://yann.lecun.com/exdb/mnist/
dc.contributor.author Kohler, Michael
dc.contributor.author Walter, Benjamin
dc.date.accessioned 2023-05-27T16:28:00Z
dc.date.available 2023-05-27T16:28:00Z
dc.date.created 2023-05-27
dc.date.issued 2023-05-27
dc.description This repository contains the Python code required to reproduce the simulation part of the paper "Analysis of convolutional neural network image classifiers in a rotationally symmetric model" from Kohler and Walter (2023) 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 mnist-rot image dataset consisting of the real images from which the classes "four" and "nine" were used can be downloaded from the link given below. The paper by Larochelle et al. (2007) linked below describes the dataset in more detail. The link for the original mnist dataset has also been linked below. de_DE
dc.identifier.uri https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3871
dc.identifier.uri https://doi.org/10.48328/tudatalib-1160
dc.language.iso en de_DE
dc.rights.licenseCC-BY-4.0 (https://creativecommons.org/licenses/by/4.0)
dc.subject.classification 3.31-01
dc.subject.ddc 510
dc.title Analysis of convolutional neural network image classifiers in a rotationally symmetric model: Implementations of the estimates and links to image data sets de_DE
dc.type Software de_DE
dcterms.accessRights openAccess
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
tuda.history.classification Version=2020-2024;312-01 Mathematik
tuda.unit TUDa

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Rate of convergence of image classifiers based on convolutional neural networks: Implementations of the estimates and links to image data sets