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Related Experiment Video

Updated: Jan 3, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Noise-boosted bidirectional backpropagation and adversarial learning.

Olaoluwa Adigun1, Bart Kosko1

  • 1Department of Electrical and Computer Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|November 23, 2019
PubMed
Summary

Bidirectional backpropagation enhances neural network training by using both forward and backward passes. Special injected noise improves training speed and accuracy for tasks like image recognition and adversarial networks.

Keywords:
Bidirectional associative memoryBidirectional backpropagationExpectation–Maximization algorithmNeural networksNoise benefitStochastic resonance

Related Experiment Videos

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Traditional backpropagation is a cornerstone of neural network training.
  • Existing methods can face challenges with training speed and accuracy.
  • Generalized expectation-maximization provides a framework for understanding training dynamics.

Purpose of the Study:

  • To introduce and evaluate bidirectional backpropagation (BBP) as an enhanced training algorithm.
  • To investigate the impact of injected noise on BBP's performance.
  • To explore BBP's effectiveness in classification, regression, and adversarial network settings.

Main Methods:

  • Implementing backpropagation in both forward and backward network directions using identical synaptic weights.
  • Introducing specialized noise injection tailored for classification and regression tasks.
  • Ensuring backpropagation invariance for gradient log-likelihood calculations.
  • Applying BBP to MNIST and CIFAR-10 datasets, and to adversarial networks.

Main Results:

  • Bidirectional backpropagation demonstrated improved training time and accuracy.
  • Injected noise significantly accelerated convergence and boosted performance on image datasets.
  • BBP effectively reduced mode collapse in adversarial networks.
  • The method showed promise for both standard and Wasserstein adversarial networks.

Conclusions:

  • Bidirectional backpropagation offers a more efficient and accurate training paradigm.
  • The strategic use of injected noise is crucial for optimizing BBP performance.
  • BBP presents a valuable advancement for deep learning applications, including adversarial learning.