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Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling

arXiv 2106.05233 ↗First posted 31 May 2021cs.CV cs.LG stat.MLcited by 2

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Abstract (arXiv)

Image classification is considered, and a hierarchical max-pooling model with additional local pooling is introduced. Here the additional local pooling enables the hierachical model to combine parts of the image which have a variable relative distance towards each other. Various convolutional neural network image classifiers are introduced and compared in view of their rate of convergence. The finite sample size performance of the estimates is analyzed by applying them to simulated and real data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 31 May 2021.

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16 of 32 references.

References · 16

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4 more
  1. 2018A comparison of deep networks with ReLU activation function and linear spline-type methodscited by 18
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Analysis of convolutional neural network image classifiers in a rotationally symmetric modelcited by 4
  2. 2024Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descentcited by 0