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

05/31/2021
by   Benjamin Walter, et al.
0

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.

READ FULL TEXT

page 10

page 23

research
05/11/2022

Analysis of convolutional neural network image classifiers in a rotationally symmetric model

Convolutional neural network image classifiers are defined and the rate ...
research
07/26/2019

Universal Pooling -- A New Pooling Method for Convolutional Neural Networks

Pooling is one of the main elements in convolutional neural networks. Th...
research
03/03/2020

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

Image classifiers based on convolutional neural networks are defined, an...
research
03/02/2022

The Theoretical Expressiveness of Maxpooling

Over the decade since deep neural networks became state of the art image...
research
06/02/2020

Studying The Effect of MIL Pooling Filters on MIL Tasks

There are different multiple instance learning (MIL) pooling filters use...
research
06/30/2012

Differentiable Pooling for Hierarchical Feature Learning

We introduce a parametric form of pooling, based on a Gaussian, which ca...
research
06/02/2014

Generalized Max Pooling

State-of-the-art patch-based image representations involve a pooling ope...

Please sign up or login with your details

Forgot password? Click here to reset