Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks

Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks

21 Jun 2024 | Minjong Cheon
The paper "Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks" by Minjong Cheon explores the potential of Kolmogorov-Arnold Networks (KANs) in vision tasks. KANs, which combine the advantages of splines and multilayer perceptrons (MLPs), are proposed as a potential alternative to MLPs. The study uses the KAN-Mixer architecture, which operates directly on patches and maintains an equal resolution and size representation throughout all levels, to evaluate KANs' performance on the MNIST, CIFAR10, and CIFAR100 datasets. Key findings include: - KAN-Mixer outperforms the original MLP-Mixer on CIFAR10 and CIFAR100 datasets. - It performs slightly worse than the state-of-the-art ResNet-18. - The optimal hyperparameters for the KAN layer are n_channels = 64 and n_hiddens = 128, achieving a balanced performance and resource utilization. - KAN-Mixer shows competitive performance, especially on the MNIST dataset, with a test accuracy of 98.16%. The study highlights the promise of KANs in vision tasks and suggests that further modifications could enhance their performance. The research also provides extensive empirical assessments and compares KAN-Mixer with other models like MLP-Mixer, CNNs, and Vision Transformers (ViTs), laying the foundation for future studies on KANs.The paper "Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks" by Minjong Cheon explores the potential of Kolmogorov-Arnold Networks (KANs) in vision tasks. KANs, which combine the advantages of splines and multilayer perceptrons (MLPs), are proposed as a potential alternative to MLPs. The study uses the KAN-Mixer architecture, which operates directly on patches and maintains an equal resolution and size representation throughout all levels, to evaluate KANs' performance on the MNIST, CIFAR10, and CIFAR100 datasets. Key findings include: - KAN-Mixer outperforms the original MLP-Mixer on CIFAR10 and CIFAR100 datasets. - It performs slightly worse than the state-of-the-art ResNet-18. - The optimal hyperparameters for the KAN layer are n_channels = 64 and n_hiddens = 128, achieving a balanced performance and resource utilization. - KAN-Mixer shows competitive performance, especially on the MNIST dataset, with a test accuracy of 98.16%. The study highlights the promise of KANs in vision tasks and suggests that further modifications could enhance their performance. The research also provides extensive empirical assessments and compares KAN-Mixer with other models like MLP-Mixer, CNNs, and Vision Transformers (ViTs), laying the foundation for future studies on KANs.
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