IvanDrokin/torch-conv-kan

• highreadme#1

  Reposition the README's opening paragraph to highlight its unique value

  Why:

  CURRENT

  This project introduces and demonstrates the training, validation, and quantization of the Convolutional KAN model using PyTorch with CUDA acceleration. The `torch-conv-kan` evaluates performance on the MNIST, CIFAR, TinyImagenet and Imagenet1k datasets.

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  This project introduces `torch-conv-kan`, a PyTorch implementation of **Convolutional Kolmogorov-Arnold Networks (KANs)**, offering a novel and interpretable alternative to traditional CNNs for computer vision tasks. It provides comprehensive tools for training, validation, and quantization, with demonstrated performance on datasets like MNIST, CIFAR, TinyImageNet, and ImageNet1k.

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• mediumhomepage#2

  Add the arXiv paper link to the repository homepage

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  https://arxiv.org/abs/2407.01092

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• lowtopics#3

  Expand repository topics with more specific keywords

  Why:

  CURRENT

  computer-vision, convolutional-neural-networks, kolmogorov-arnold-networks

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  computer-vision, convolutional-neural-networks, kolmogorov-arnold-networks, deep-learning, pytorch, image-classification, neural-networks, kan

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Looking for PyTorch implementations of Kolmogorov-Arnold networks for computer vision tasks.

  you: not recommended
  AI recommended (in order):

  1. PyTorch-KAN (Blealtan/pytorch-kan)
  2. KANs (ZiyaoLi/KANs)
  3. KAN-PyTorch (GistNoesis/KAN-PyTorch)
  4. Implementations within Research Papers

  AI recommended 4 alternatives but never named IvanDrokin/torch-conv-kan. This is the gap to close.

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• CATEGORY QUERY

  Need to explore novel convolutional neural network architectures beyond standard CNNs for image classification.

  you: not recommended
  AI recommended (in order):

  1. Vision Transformer (ViT)
  2. Swin Transformer
  3. ConvNeXt
  4. EfficientNet
  5. NASNet
  6. EfficientDet
  7. Xception
  8. MobileNetV1
  9. MobileNetV2
  10. MobileNetV3
  11. ResNeXt

  AI recommended 11 alternatives but never named IvanDrokin/torch-conv-kan. This is the gap to close.

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• Metadata completeness

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  Suggestion:

• README presence

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• Compared to common alternatives in this category, what is the core differentiator of IvanDrokin/torch-conv-kan?

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  ✓AI named IvanDrokin/torch-conv-kan explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts IvanDrokin/torch-conv-kan in production, what risks or prerequisites should they evaluate first?

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  ⚠AI did not name IvanDrokin/torch-conv-kan — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo IvanDrokin/torch-conv-kan solve, and who is the primary audience?

  pass
  ⚠AI did not name IvanDrokin/torch-conv-kan — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?