# On the rate of convergence of image classifiers based on convolutional neural networks

Source: [On the rate of convergence of image classifiers based on convolutional neural networks](<https://www.sota2.com/research/paper/68fa68801e209131cea1c6fd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:38:59.898Z

Authors: M. Kohler, A. Krzyzak, B. Walter

Published: 2020-03-03T14:24:24.000Z

## Abstract

Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of the aposteriori probability a rate of convergence is shown which is independent of the dimension of the image. This proves that in image classification it is possible to circumvent the curse of dimensionality by convolutional neural networks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.01526>)

[PDF](<https://arxiv.org/pdf/2003.01526.pdf>)