Harmonic Machine Learning Models are Robust
We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicat...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
29.04.2024
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Subjects | |
Online Access | Get full text |
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Summary: | We introduce Harmonic Robustness, a powerful and intuitive method to test the
robustness of any machine-learning model either during training or in black-box
real-time inference monitoring without ground-truth labels. It is based on
functional deviation from the harmonic mean value property, indicating
instability and lack of explainability. We show implementation examples in
low-dimensional trees and feedforward NNs, where the method reliably identifies
overfitting, as well as in more complex high-dimensional models such as
ResNet-50 and Vision Transformer where it efficiently measures adversarial
vulnerability across image classes. |
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DOI: | 10.48550/arxiv.2404.18825 |