Documentdetail
ID kaart
oai:arXiv.org:2406.09087
Onderwerp
Computer Science - Computer Vision... Computer Science - Artificial Inte...Auteur
Azam, Basim Akhtar, NaveedCategorie
Computer Science
Jaar
2024
vermelding datum
23-10-2024
Trefwoorden
vision networks kans computerBeschrijving
Kolmogorov-Arnold Networks (KANs) introduce a paradigm of neural modeling that implements learnable functions on the edges of the networks, diverging from the traditional node-centric activations in neural networks.
This work assesses the applicability and efficacy of KANs in visual modeling, focusing on fundamental recognition and segmentation tasks.
We mainly analyze the performance and efficiency of different network architectures built using KAN concepts along with conventional building blocks of convolutional and linear layers, enabling a comparative analysis with the conventional models.
Our findings are aimed at contributing to understanding the potential of KANs in computer vision, highlighting both their strengths and areas for further research.
Our evaluation point toward the fact that while KAN-based architectures perform in line with the original claims, it may often be important to employ more complex functions on the network edges to retain the performance advantage of KANs on more complex visual data.
Azam, Basim,Akhtar, Naveed, 2024, Suitability of KANs for Computer Vision: A preliminary investigation
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
unizero latent learning
Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping
mapping motor-eloquent threshold anisotropy spherical gliomas patients corticospinal tract tractography
