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Related Experiment Video

Updated: Oct 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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An Adaptive Deep Learning Optimization Method Based on Radius of Curvature.

Jiahui Zhang1, Xinhao Yang1, Ke Zhang1

  • 1School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215006, China.

Computational Intelligence and Neuroscience
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces adaptive clamping methods for deep neural networks to reduce oscillations and improve training. These novel techniques enhance convergence speed and accuracy in deep learning models.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep neural networks (DNNs) often suffer from local optimal oscillation during training.
  • Existing optimization methods may struggle to navigate complex loss landscapes effectively.

Purpose of the Study:

  • To develop novel adaptive methods to alleviate local optimal oscillation in DNNs.
  • To enhance the convergence speed and accuracy of deep learning model training.

Main Methods:

  • Introduced an adaptive clamping method (SGD-MS) using the radius of curvature of the objective function.
  • Utilized the radius of curvature as a threshold to adaptively manage momentum and gradient terms.
  • Proposed an accelerated version (SGD-MA) incorporating aggregated momentum for faster convergence.
  • Developed a new parameter updating algorithm for DNNs.

Main Results:

  • The proposed SGD-MS and SGD-MA methods effectively mitigate local optimal oscillation.
  • Significant improvements in convergence speed and model accuracy were observed across various datasets.
  • The novel parameter updating algorithm contributes to more stable and efficient DNN training.

Conclusions:

  • Adaptive clamping based on the radius of curvature is a promising approach for improving DNN training stability.
  • The developed methods offer enhanced performance compared to standard optimization techniques.
  • This research provides valuable contributions to the field of deep learning optimization.