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A Geometric Perspective on Information Plane Analysis.

Mina Basirat1, Bernhard C Geiger2, Peter M Roth3

  • 1Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16/II, 8010 Graz, Austria.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

Information plane analysis, using mutual information, helps understand neural network training. A new geometric interpretation resolves inconsistencies, revealing insights into overfitting, underfitting, and noisy data effects.

Keywords:
adaptive and fixed binningimage classificationinformation plane analysisneural networks

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Information Theory

Background:

  • Information plane analysis estimates mutual information to study neural network training.
  • Current methods face challenges with continuous-valued activations, leading to inconsistent results.
  • Existing literature shows conflicting findings due to estimation complexities.

Purpose of the Study:

  • To demonstrate the value of information plane analysis in neural network training.
  • To address inconsistencies in current information plane analysis literature.
  • To provide a robust framework for interpreting neural network learning dynamics.

Main Methods:

  • Complementing the binning estimator with a geometric interpretation of mutual information.
  • Evaluating the impact of regularization techniques on information planes.
  • Analyzing neural network behavior with noisy data and labels.

Main Results:

  • The geometric interpretation reconciles previous inconsistencies in information plane analysis.
  • Underfitting and overfitting phenomena are clearly interpretable through the geometric lens.
  • The study provides insights into how neural networks learn from noisy data and labels.

Conclusions:

  • Information plane analysis, enhanced by a geometric view, is a valuable tool for understanding neural network training.
  • This approach offers a clearer interpretation of model behavior, including generalization.
  • The findings contribute to more robust and interpretable deep learning models.