Pith. sign in

REVIEW 7 cited by

Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.01092 v1 pith:4WKRZJFH submitted 2024-07-01 cs.CV cs.AIcs.LG

Ivan Drokin This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords convolutionalkansdesignkolmogorov-arnoldlayersmodelstaskscomputer
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The emergence of Kolmogorov-Arnold Networks (KANs) has sparked significant interest and debate within the scientific community. This paper explores the application of KANs in the domain of computer vision (CV). We examine the convolutional version of KANs, considering various nonlinearity options beyond splines, such as Wavelet transforms and a range of polynomials. We propose a parameter-efficient design for Kolmogorov-Arnold convolutional layers and a parameter-efficient finetuning algorithm for pre-trained KAN models, as well as KAN convolutional versions of self-attention and focal modulation layers. We provide empirical evaluations conducted on MNIST, CIFAR10, CIFAR100, Tiny ImageNet, ImageNet1k, and HAM10000 datasets for image classification tasks. Additionally, we explore segmentation tasks, proposing U-Net-like architectures with KAN convolutions, and achieving state-of-the-art results on BUSI, GlaS, and CVC datasets. We summarized all of our findings in a preliminary design guide of KAN convolutional models for computer vision tasks. Furthermore, we investigate regularization techniques for KANs. All experimental code and implementations of convolutional layers and models, pre-trained on ImageNet1k weights are available on GitHub via this https://github.com/IvanDrokin/torch-conv-kan

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 7 Pith papers

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

  1. KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

    cs.AR 2025-12 conditional novelty 7.0 of 10

    Quantized, pruned Kolmogorov-Arnold Networks can be compiled directly into FPGA lookup tables, achieving extreme latency/resource reductions and matching state-of-the-art LUT-based networks on several benchmarks.

  2. KAN-SAs: Efficient Acceleration of Kolmogorov-Arnold Networks on Systolic Arrays

    cs.AR 2025-11 conditional novelty 7.0 of 10

    A systolic-array accelerator that tabulates B-splines and exploits B-spline local support achieves ~100% PE utilization and a 2x cycle reduction for KAN inference compared with a conventional systolic array.

  3. AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A new 30-task benchmark shows LLM agents can improve real ML experiments through sequential hyperparameter choices, but their gains are uneven and often not retained.

  4. Improving Memory Efficiency for Training KANs via Meta Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    MetaKANs generates each KAN activation function from a shared prompt-conditioned meta-learner, cutting trainable parameters toward MLP level while retaining comparable or better accuracy on tested benchmarks.

  5. Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks

    cs.LG 2025-02 conditional novelty 5.0 of 10

    A low tensor-rank adaptation (LoTRA) method and learning-rate guidance enable efficient fine-tuning of Kolmogorov-Arnold networks, validated on PDE solving and representation tasks.

  6. 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.

  7. SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

    eess.IV 2024-12 conditional novelty 4.0 of 10

    A new segmentation architecture combining Fourier-based KAN convolution and a gated recurrent patch-sequence module improves hepatic vessel CT segmentation Dice by 1.78 points over TransUNet on one benchmark.

Pith tools