Journal Article

PFSKANs: A Novel Pixel-Level Feature Selection Model Based on Kolmogorov–Arnold Networks

Yang, Rui, Zeng, Hongzheng, Yao, Guangzhe, Basin, Michael V.

2025

Overview

Inspired by the interpretability of Kolmogorov–Arnold Networks (KANs), a novel Pixel-level Feature Selection (PFS) model based on KANs (PFSKANs) is proposed as a fundamentally distinct alternative from trainable Convolutional Neural Networks (CNNs) and transformers in the computer vision tasks. We modify the simplification techniques of KANs to detect key pixels with high contribution scores directly at the input image. Specifically, a trainable selection procedure is intuitively visualized and performed only once, since the obtained interpretable pixels can subsequently be identified and dimensionally standardized using the proposed mathematical approach. Experiments on the image classification tasks using the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that PFSKANs achieve comparable performance to CNNs in terms of accuracy, parameter efficiency, and training time.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Analysis / Classification / Computer vision / Datasets / Deep learning / Entropy / feature extraction / Feature selection / Innovations / kolmogorov-arnold networks / Machine vision / Neural networks / pixel-level feature