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Comparison of Generalized Learning Vector Quantization learning dynamic and numerical stability regarding the Crammer-normalization and the Hein-normalization for adversarial robustness

  • Adversarial robustness of a nearest prototype classifier assures safe deployment in sensitive use fields. Much research has been conducted on artificial neural networks regarding their robustness against adversarial attacks, whereas nearest prototype classifiers have not chalked similar successes. This thesis presents the learning dynamics and numerical stability regarding the Crammer-normalization and the Hein-normalization for adversarial robustness of nearest prototype classifiers. Results of conducted experiments are penned down and analyzed to ascertain the bounds given by Saralajew et al. and Hein et al. for adversarial robustness of nearest prototype classifiers.

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Metadaten
Author:Asirifi Boa
Advisor:Thomas Villmann, Marika Kaden
Document Type:Master's Thesis
Language:English
Year of first Publication:2023
Publishing Institution:Hochschule Mittweida
Release Date:2024/02/02
GND Keyword:Vektorquantisierung; Neuronales Netz
Page Number:37
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:621.3822 digitale Signalverarbeitung, Vektorquantisierung