detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2410.05500
Tema
Computer Science - Computer Vision... Computer Science - Artificial Inte... Computer Science - Machine Learnin... I.2.10 I.4.10 I.4.3 I.4.9Autor
Yu, Ray Congrui Wu, Sherry Gui, JiangCategoría
Computer Science
Año
2024
fecha de cotización
12/3/2025
Palabras clave
network layers science computerResumen
Despite their immense success, deep neural networks (CNNs) are costly to train, while modern architectures can retain hundreds of convolutional layers in network depth.
Standard convolutional operations are fundamentally limited by their linear nature along with fixed activations, where multiple layers are needed to learn complex patterns, making this approach computationally inefficient and prone to optimization difficulties.
As a result, we introduce RKAN (Residual Kolmogorov-Arnold Network), which could be easily implemented into stages of traditional networks, such as ResNet.
The module also integrates polynomial feature transformation that provides the expressive power of many convolutional layers through learnable, non-linear feature refinement.
Our proposed RKAN module offers consistent improvements over the base models on various well-known benchmark datasets, such as CIFAR-100, Food-101, and ImageNet.
;Comment: Code is available at https://github.com/withray/residualKAN.git
Yu, Ray Congrui,Wu, Sherry,Gui, Jiang, 2024, Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
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