用新型KAN网络替代MLP，提升图像分类精度与可解释性
KAN-Mixers: a new deep learning architecture for image classification
- 以KAN替代MLP作为核心组件，构建新混合架构KAN-Mixers
- 在Fashion-MNIST和CIFAR-10上分别达到90.30%和69.80%平均准确率
- 兼顾性能与可解释性，适合追求模型透明性的视觉任务研究
由于出色的性能，卷积神经网络（CNN）和视觉变压器（ViT）已成为解决计算机视觉任务的标准架构。这些架构依赖大规模数据集以及卷积和自注意力操作。2021年，MLP-Mixer问世，仅基于多层感知机（MLP）便取得了与CNN和ViT相当的优异表现。然而，其在图像精细化特征提取方面仍存在局限。最近提出的科尔莫戈罗夫-阿诺德网络（KAN）被视为替代MLP的有前景方案，相比传统MLP，在准确性和可解释性方面更具优势。本文旨在设计一种基于KAN的新混合架构——KAN-Mixers，并在图像分类任务中评估其多种性能指标。实验结果表明，该模型在Fashion-MNIST和CIFAR-10数据集上的平均准确率分别达到90.30%和69.80%，优于MLP、MLP-Mixer和原始KAN模型。
原文摘要 · Abstract (English)
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.
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