mlsanityData to 13 Oct 2026

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

arXiv 2003.01526 ↗First posted 3 Mar 2020stat.ML cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). First posted on arXiv 3 Mar 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2; 2025: 1.

7 papers.

  1. 2021Convergence rates of deep ReLU networks for multiclass classificationcited by 21
  2. 2022What Can Be Learnt With Wide Convolutional Neural Networks?cited by 16
  3. 2024Misclassification bounds for PAC-Bayesian sparse deep learningcited by 7
  4. 2022Analysis of convolutional neural network image classifiers in a rotationally symmetric modelcited by 4
  5. 2021Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local poolingcited by 2
  6. 2025Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionalitycited by 2
  7. 2024Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descentcited by 0