mlsanityData to 14 Oct 2026

Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision

arXiv 2411.06727 ↗First posted 11 Nov 2024cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its convolutional variants in image classification and semantic segmentation. The focus is placed on examining their characteristics across varying data scales and noise levels. Results indicate that while KAN exhibits stronger fitting capabilities, it is highly sensitive to noise, limiting its robustness. To address this challenge, we propose a smoothness regularization method and introduce a Segment Deactivation technique. Both approaches enhance KAN's stability and generalization, demonstrating its potential in handling complex visual data tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

20 of 34 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  7. 2020End-to-End Object Detection with Transformerscited by 7,414
  8. 2016Densely Connected Convolutional Networkscited by 6,720
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  10. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  11. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  12. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
8 more
  1. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  2. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
  3. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,101
  4. 2024KAN: Kolmogorov-Arnold Networkscited by 649
  5. 2024Wav-KAN: Wavelet Kolmogorov-Arnold Networkscited by 122
  6. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 98
  7. 2024A Temporal Kolmogorov-Arnold Transformer for Time Series Forecastingcited by 52
  8. 2024KAGNNs: Kolmogorov-Arnold Networks meet Graph Learningcited by 49

Built on by

Not cited yet.