KAN在图像分类中表现有潜力但计算成本高，需改进才适合大规模视觉任务。
KANs for Computer Vision: An Experimental Study
- 用可学习的激活函数替代传统固定激活，提升非线性表达灵活性。
- 在特定视觉任务上性能接近经典模型，但超参数敏感且耗时更长。
- 适合关注KAN机制研究或探索新型神经网络架构的研究者参考。
本文对柯尔莫哥洛夫-阿诺德网络（KANs）在计算机视觉任务中的应用进行了实验研究，重点关注图像分类。与传统多层感知机（MLPs）和卷积神经网络（CNNs）使用预设激活函数不同，KANs在边上线性组合后引入可学习的激活函数，提供更灵活的非线性变换。尽管KANs在简单或小规模数据集上已显示出潜力，但在复杂真实场景下的有效性仍不明确。本研究通过实证分析揭示：尽管在某些视觉任务中表现良好，但KANs面临显著的超参数敏感性和更高的计算开销。这些挑战表明，为应对大规模视觉问题，需要对KANs进行结构改造，如与其他架构融合。本研究聚焦于经验观察，不提出新方法，旨在为未来优化KANs，特别是在计算机视觉等领域的应用提供参考。
原文摘要 · Abstract (English)
This paper presents an experimental study of Kolmogorov-Arnold Networks (KANs) applied to computer vision tasks, particularly image classification. KANs introduce learnable activation functions on edges, offering flexible non-linear transformations compared to traditional pre-fixed activation functions with specific neural work like Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). While KANs have shown promise mostly in simplified or small-scale datasets, their effectiveness for more complex real-world tasks such as computer vision tasks remains less explored. To fill this gap, this experimental study aims to provide extended observations and insights into the strengths and limitations of KANs. We reveal that although KANs can perform well in specific vision tasks, they face significant challenges, including increased hyperparameter sensitivity and higher computational costs. These limitations suggest that KANs require architectural adaptations, such as integration with other architectures, to be practical for large-scale vision problems. This study focuses on empirical findings rather than proposing new methods, aiming to inform future research on optimizing KANs, in particular computer vision applications or alike.
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