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

Kohler, Michael; Walter, Benjamin

Open Access

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

Files

Implementations.zip (165.33 KB)

Date

2023-05-27

Type

Software

Authors

Kohler, Michael
Walter, Benjamin

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.