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Fisher information and the geometry of memorization in neural networks

L5 · ResearcherResearcharXiv· 10/12/2026

Provides theoretical insights into neural network capacity allocation between learning and memorization for ML researchers.

AI Summary

Researchers demonstrate how Fisher information geometry separates generalization from memorization in neural networks using CIFAR-10 and experiments.

Excerpt

A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from idiosyncratic or even corrupted features of training data. Fisher information has emerged as a diagnostic tool for this separation, leveraging loss geometry to distinguish generalization from memorization. Here we investigate this connection in vision models trained on CIFAR-10 with controlled noise that can be learned only through memorization. We

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