# Kolmogorov-Arnold networks for metal surface defect classification

Source: [Kolmogorov-Arnold networks for metal surface defect classification](<https://www.sota2.com/research/paper/68fa76ae1e209131cea6e69a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:59:18.940Z

Authors: Maciej Krzywda, Mariusz Wermi\\'nski, Szymon {\\L}ukasik, Amir H. Gandomi

Published: 2025-01-10T23:58:30.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.06389>)

[PDF](<https://arxiv.org/pdf/2501.06389.pdf>)