Harmonic Machine Learning Models are Robust
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917652719468544 |
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| author | Kersting, Nicholas S. Li, Yi Mohanty, Aman Obisesan, Oyindamola Okochu, Raphael |
| author_facet | Kersting, Nicholas S. Li, Yi Mohanty, Aman Obisesan, Oyindamola Okochu, Raphael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harmonic Machine Learning Models are Robust Kersting, Nicholas S. Li, Yi Mohanty, Aman Obisesan, Oyindamola Okochu, Raphael Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Harmonic Machine Learning Models are Robust |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.18825 |