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Updated: May 10, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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A lightweight and gradient-stable neural layer.

Yueyao Yu1, Yin Zhang2

  • 1School of Science and Engineering, The Chinese University of Hong Kong-Shenzhen, China; Shenzhen Research Institute of Big Data, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 11, 2024
PubMed
Summary
This summary is machine-generated.

We introduce the Householder-absolute neural layer (Han-layer) for efficient neural networks. This new layer reduces parameters and computation while ensuring stable gradients for improved model performance.

Keywords:
Deep neural networkGradient stabilityLightweight modelLow complexity

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Neural network efficiency and deployability are critical challenges.
  • Fully connected layers (FC) are computationally intensive, with O(d^2) complexity.
  • Gradient stability is essential for effective deep learning model training.

Purpose of the Study:

  • To propose a novel neural layer architecture, the Householder-absolute neural layer (Han-layer).
  • To enhance resource efficiency and model deployability in neural networks.
  • To address computational complexity and gradient stability issues in deep learning.

Main Methods:

  • Introducing the Householder-absolute neural layer (Han-layer) architecture.
  • Utilizing Householder weighting and absolute-value activation functions.
  • Analyzing parameter reduction from O(d^2) to O(d) compared to FC layers.
  • Ensuring gradient stability through orthogonal Jacobian properties.

Main Results:

  • Han-layer reduces parameters and computational complexity significantly.
  • The architecture guarantees orthogonal Jacobians, ensuring gradient stability.
  • Replacing FC layers with Han-layers maintains or improves generalization performance.
  • Numerical experiments validate the efficiency and effectiveness of Han-layers.

Conclusions:

  • Han-layers offer a promising approach for resource-efficient and deployable neural networks.
  • The proposed architecture effectively mitigates gradient vanishing/exploding issues.
  • Han-layers present a viable alternative to traditional FC layers, enhancing model performance.