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Dependent Competing Failure Processes in Reliability Systems.

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Uncovering Neural Learning Dynamics Through Latent Mutual Information.

Arianna Issitt1, Alex Merino1, Lamine Deen1

  • 1NEural TransmissionS (NETS) Lab, Florida Institute of Technology, Melbourne, FL 32901, USA.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Convolutional neural networks concentrate information in specific channels during learning, rather than universally compressing it. This selective concentration improves accuracy and speeds up training.

Keywords:
HSIC regularizationXAIchannel specializationdeep learninginformation theoryinterpretabilitylearning dynamicsmutual informationrepresentation learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Convolutional Neural Networks (CNNs) are key to image classification.
  • Understanding information flow within CNNs during learning is crucial.

Purpose of the Study:

  • To investigate how CNNs reorganize information during natural image classification.
  • To analyze the role of mutual information (MI) in CNN representations.

Main Methods:

  • Tracking mutual information between inputs, intermediate representations, and labels in VGG-16, ResNet-18, and ResNet-50.
  • Analyzing information concentration in specific channels using knockouts, shuffles, and perturbations.
  • Implementing a dependence-aware regularizer based on the Hilbert-Schmidt Independence Criterion.

Main Results:

  • Label-relevant MI increases with network depth, while input MI varies with architecture.
  • Information concentrates in a subset of channels, which are functionally necessary for accuracy.
  • A regularizer promoting selective concentration improves convergence and accuracy.

Conclusions:

  • CNN representation learning is driven by selective concentration and decorrelation, not global compression.
  • Targeted regularization can guide CNNs towards efficient information processing.
  • Findings offer insights into designing more effective deep learning models.