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Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks

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arxiv 2406.14916 v1 pith:Z5CRDC52 submitted 2024-06-21 cs.CV cs.AIcs.LG

Minjong Cheon This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords tasksvisionalternativecifar10cifar100futurekolmogorov-arnoldmlp-mixer
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In the realm of deep learning, the Kolmogorov-Arnold Network (KAN) has emerged as a potential alternative to multilayer projections (MLPs). However, its applicability to vision tasks has not been extensively validated. In our study, we demonstrated the effectiveness of KAN for vision tasks through multiple trials on the MNIST, CIFAR10, and CIFAR100 datasets, using a training batch size of 32. Our results showed that while KAN outperformed the original MLP-Mixer on CIFAR10 and CIFAR100, it performed slightly worse than the state-of-the-art ResNet-18. These findings suggest that KAN holds significant promise for vision tasks, and further modifications could enhance its performance in future evaluations.Our contributions are threefold: first, we showcase the efficiency of KAN-based algorithms for visual tasks; second, we provide extensive empirical assessments across various vision benchmarks, comparing KAN's performance with MLP-Mixer, CNNs, and Vision Transformers (ViT); and third, we pioneer the use of natural KAN layers in visual tasks, addressing a gap in previous research. This paper lays the foundation for future studies on KANs, highlighting their potential as a reliable alternative for image classification tasks.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks

    cs.LG 2025-01 conditional novelty 6.0 of 10

    PRKAN lowers KAN parameter counts to near-MLP levels via attention, convolution/pooling, dimension summation, and feature-vector projections, reaching MLP-like accuracy on MNIST and Fashion-MNIST.

  2. Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Training in a B-spline KAN basis is equivalent to preconditioned gradient descent on a multichannel ReLU MLP, and geometric refinement plus trainable knots accelerate and improve training.

  3. Efficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification

    cs.CV 2025-01 reject novelty 5.0 of 10

    CKANs are measurably less efficient than standard CNNs, and on ImageNet the accuracy gap is large, but the paper's baseline and timing comparisons are not controlled.

  4. SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions

    cs.LG 2026-06 conditional novelty 4.0 of 10

    SechKAN combines sech basis functions with a 1D linear projection to build a KAN-style model whose parameter count matches MLPs and which is competitive or better than several KAN variants on tested benchmarks.

  5. Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification

    eess.IV 2025-07 conditional novelty 4.0 of 10

    A hybrid Capsule-ConvKAN model reports 91.21% accuracy on histopathological image classification, outperforming CNN, CapsNet, and ConvKAN baselines on a single dataset.

  6. Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies

    cs.LG 2025-05 conditional novelty 4.0 of 10

    KANs achieve higher or comparable accuracy to MLPs across four tabular datasets in simulated federated learning, using fewer communication rounds.

  7. Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

    cs.LG 2025-04 conditional novelty 4.0 of 10

    Conformal prediction applied to ensembles of KANs, FBKANs, and MFKANs yields prediction intervals that empirically hit the target 95% coverage on four synthetic problems.

  8. Bridging KAN and MLP: MJKAN, a Hybrid Architecture with Both Efficiency and Expressiveness

    cs.LG 2025-07 reject novelty 3.0 of 10

    MJKAN is a FiLM-modulated RBF layer that beats MLPs on some 1D regression tasks with carefully chosen basis counts, but underperforms MLPs on classification benchmarks.

  9. KAT to KANs: A Review of Kolmogorov-Arnold Networks and the Neural Leap Forward

    cs.LG 2024-11 reject

    A review of Kolmogorov-Arnold Networks that restates existing theory and claims, without new experiments or derived results.

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