AntonioTepsich/Convolutional-KANs
This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.
This project offers an alternative way to process images in deep learning models by replacing traditional convolutional layers with a new type of layer called a Convolutional Kolmogorov-Arnold Network (CKAN). Instead of standard linear operations, CKANs use learnable, non-linear functions at each pixel, which can help models better understand complex patterns in visual data. It's designed for machine learning researchers and practitioners who are experimenting with novel neural network architectures for image-related tasks.
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Use this if you are a machine learning researcher or practitioner interested in exploring new neural network architectures for image classification and want to leverage the theoretical benefits of Kolmogorov-Arnold Networks in convolutional settings.
Not ideal if you need models with extremely fast training times or if you prioritize minimizing parameter count for image classification, as CKANs currently have longer training times and more parameters per layer than traditional CNNs.
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MIT
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Last pushed
Apr 08, 2025
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