Kolmogorov-Arnold networks for metal surface defect classification
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909460261240832 |
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| author | Krzywda, Maciej Wermiński, Mariusz Łukasik, Szymon Gandomi, Amir H. |
| author_facet | Krzywda, Maciej Wermiński, Mariusz Łukasik, Szymon Gandomi, Amir H. |
| contents | This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multilayer perceptrons (MLPs), facilitating more efficient function approximation by utilizing spline functions. The results show that KAN networks can achieve better accuracy than convolutional neural networks (CNNs) with fewer parameters, resulting in faster convergence and improved performance in image classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kolmogorov-Arnold networks for metal surface defect classification Krzywda, Maciej Wermiński, Mariusz Łukasik, Szymon Gandomi, Amir H. Machine Learning Artificial Intelligence Neural and Evolutionary Computing This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multilayer perceptrons (MLPs), facilitating more efficient function approximation by utilizing spline functions. The results show that KAN networks can achieve better accuracy than convolutional neural networks (CNNs) with fewer parameters, resulting in faster convergence and improved performance in image classification. |
| title | Kolmogorov-Arnold networks for metal surface defect classification |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2501.06389 |