Due to their effective performance, Convolutional Neural Network (CNN) and Vision Transformer (ViT) architectures have become the standard for solving computer vision tasks. Such architectures require large data sets and rely on convolution and self-attention operations. In 2021, MLP-Mixer emerged, an architecture that relies only on Multilayer Perceptron (MLP) and achieves extremely competitive results when compared to CNNs and ViTs. Despite its good performance in computer vision tasks, the MLP-Mixer architecture may not be suitable for refined feature extraction in images. Recently, the Kolmogorov-Arnold Network (KAN) was proposed as a promising alternative to MLP models. KANs promise to improve accuracy and interpretability when compared to MLPs. Therefore, the present work aims to design a new mixer-based architecture, called KAN-Mixers, using KANs as main layers and evaluate its performance, in terms of several performance metrics, in the image classification task. As main results obtained, the KAN-Mixers model was superior to the MLP, MLP-Mixer and KAN models in the Fashion-MNIST and CIFAR-10 datasets, with 0.9030 and 0.6980 of average accuracy, respectively.

本研究针对现有的MLP-Mixer在图像特征提取中的不足，提出了一种新型的KAN-Mixers架构，利用Kolmogorov-Arnold Network (KAN)作为主要层。通过在Fashion-MNIST和CIFAR-10数据集上的评估，KAN-Mixers在分类任务中超过了传统的MLP、MLP-Mixer和KAN模型，显示出其在准确性和可解释性上的潜力。

KAN-Mixers：一种新的深度学习架构用于图像分类