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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Analyzing Parameter-Efficient Convolutional Neural Network Architectures for Visual Classification.

Nazmul Shahadat1, Anthony S Maida2

  • 1School of Science and Mathematics, Truman State University, Kirksville, MO 63501, USA.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This review explores parameter-efficient convolutional neural networks (CNNs) to reduce computational costs. New hypercomplex and axial attention models significantly decrease parameters while maintaining high accuracy in visual recognition tasks.

Keywords:
PHM based dense layerPHM layerRCNsaxial attention networkscost effectivedeep learninghypercomplex networksparameter efficientquaternion networksrepresentation learningresidual axial networksweight sharing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning Architectures

Background:

  • Deep and wide convolutional neural networks (CNNs) dominate visual recognition but incur high computational and memory costs.
  • Parameter efficiency is crucial for deploying CNNs on resource-constrained devices.
  • Existing efficient architectures like MobileNets and SqueezeNets offer trade-offs between efficiency and accuracy.

Purpose of the Study:

  • To review recent advancements in parameter-efficient CNN design.
  • To introduce novel hypercomplex and attention-based CNN architectures.
  • To analyze the performance of these efficient models against established benchmarks.

Main Methods:

  • Exploration of hypercomplex representations with cross-channel weight sharing.
  • Implementation of axial attention mechanisms for feature representation.
  • Development of real-valued architectures utilizing separable convolutions.
  • Introduction of Full Hypercomplex Neural Networks (FHNNs), Representational Axial Attention (RepAA) models, and Separable Hypercomplex Networks (SHNNs).
  • Factorization of quaternion convolutions into sequential vectormap operations in SHNNs.
  • Comparison with MobileNets and SqueezeNets using Residual One-Dimensional Convolutional Networks (RCNs).

Main Results:

  • Hypercomplex representations, axial attention, and separable convolutions reduce parameter counts effectively.
  • FHNNs and RepAA models demonstrate enhanced efficiency.
  • SHNNs achieve approximately 50% parameter reduction by factorizing quaternion convolutions.
  • RCNs show competitive performance in image classification and super-resolution with significantly fewer parameters compared to existing models.

Conclusions:

  • Parameter-efficient CNN designs are vital for advancing visual recognition.
  • Novel hypercomplex and attention-based architectures offer significant reductions in computational overhead.
  • Future research should focus on further optimizing CNNs for efficiency without compromising accuracy.