Hot on the heels of recent advances in Kolmogorov-Arnold Networks (KANs) technology, we introduce our innovative Convolutional Kolmogorov-Arnold Network (Convolutional-KANs)!
This breakthrough extends the idea of the innovative architecture of KANs to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel
Our team has worked diligently to explore the potential of this novel architecture and has obtained promising preliminary results, with Convolutional-KANs achieving only 0.04 less accuracy with almost half the parameters of the common CNN (2-layer) and 7 times fewer parameters than a CNN (4-layer)
We invite the research and development community to explore our repository, experiment with Convolutional-KANs, and contribute to its evolution
This breakthrough extends the idea of the innovative architecture of KANs to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel
Our team has worked diligently to explore the potential of this novel architecture and has obtained promising preliminary results, with Convolutional-KANs achieving only 0.04 less accuracy with almost half the parameters of the common CNN (2-layer) and 7 times fewer parameters than a CNN (4-layer)
We invite the research and development community to explore our repository, experiment with Convolutional-KANs, and contribute to its evolution
Explore the GitHub repository! https://github.com/AntonioTepsich/Convolutional-KANs