方法对比

	Kolmogorov-Arnold Networks	Vision Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2024	2021
提出者	Ziming Liu	Dosovitskiy, A. et al.
类型	Neural network architecture	Transformer architecture for images (self-attention over patches)
开创性文献	Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
别名	KAN, Kolmogorov-Arnold	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
相关	4	5
摘要	Kolmogorov-Arnold Networks (KAN) is a neural network architecture introduced by Liu et al. in 2024 that replaces linear transformations with learned univariate functions on edges. Inspired by the Kolmogorov-Arnold representation theorem, KAN achieves superior function approximation with fewer parameters than traditional MLPs, offering potential efficiency gains and improved interpretability.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).