Analysis of convolutional neural network image classifiers in a rotationally symmetric model: Implementations of the estimates and links to image data sets
| 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.issued | 2023-05-27 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3871 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-1160 | |
| 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.language.iso | en | de_DE |
| dc.relation | Cites;DOI;https://doi.org/10.1109/TIT.2023.3262745 | |
| dc.relation | References;URL;https://sites.google.com/a/lisa.iro.umontreal.ca/public_static_twiki/variations-on-the-mnist-digits | |
| dc.relation | References;DOI;https://doi.org/10.1145/1273496.1273556 | |
| dc.relation | References;URL;http://yann.lecun.com/exdb/mnist/ | |
| dc.rights | Creative Commons Attribution 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.classification | 312-01 Mathematik | de_DE |
| 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 |
| tud.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 [3]
This collection contains the most important parts to be able to reproduce the simulation parts of the image data used in the research work of the project "Rate of convergence of image classifiers based on convolutional neural networks" funded by the German Research Foundation (DFG project number: 449102119). In addition, the collection contains references to the image datasets that were used. The collection is structured into the three papers of the project, which include simulation parts.
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0