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Updated: Oct 31, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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On Information Plane Analyses of Neural Network Classifiers-A Review.

Bernhard C Geiger

    IEEE Transactions on Neural Networks and Learning Systems
    |June 30, 2021
    PubMed
    Summary

    Information plane analyses of neural network classifiers show compression is often geometric, not information-theoretic. This finding offers new justification for information planes in machine learning research.

    Area of Science:

    • Machine Learning
    • Information Theory
    • Neural Networks

    Background:

    • Information bottleneck theory suggests a causal link between information-theoretic compression and generalization in neural networks.
    • Existing empirical evidence supporting and conflicting with this claim requires careful re-evaluation of estimation methods.
    • Information plane (IP) analyses visualize information quantities in neural network classifiers.

    Approach:

    • A comprehensive review of current literature on information plane analyses of neural network classifiers.
    • Detailed analysis of the methods used for estimating information quantities in previous studies.
    • Investigation into the nature of compression observed in information planes.

    Key Points:

    • Compression visualized in information planes is frequently geometric, relating to latent representation geometry, rather than purely information-theoretic.

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  • The data processing inequality may not hold for mutual information estimates in deterministic neural networks.
  • A fitting phase, where mutual information increases, is necessary but not sufficient for good classification performance.

    • Conclusions:

    • The geometric interpretation of compression provides renewed justification for information plane analyses.
    • Challenges in estimating mutual information in neural networks can affect the visibility of the fitting phase in information planes.
    • Further research is needed to reconcile information-theoretic compression with geometric properties in neural network generalization.