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The bottleneck KANConv is shown in the figure below. First, a 1×1 convolution is used to compress the number of feature map channels from in_channels to 3, then KANConv is applied while the size and number of channels of the feature map remain unchanged, and finally a 1×1 convolution...
A drop-in replacement for the torch.nn.Conv2d layer that uses the Kolmogorov-Arnold Network (KAN) as a convolutional kernel.Currently, supports grouped convolution, padding with different modes, dilation, and stride.The KAN implementation is taken from the https://github.com/Blealtan/efficient-...
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kanlinear import KANLinear class ConvKAN(torch.nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int or tuple = 3, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, padding_mode: str = "zeros", bias: bool = True, ...
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Bottleneck KANConv for Unet unet kan unet-pytorch kanconv bottleneck-kanconv kan-convolution Updated Jul 5, 2024 Python Improve this page Add a description, image, and links to the kanconv topic page so that developers can more easily learn about it. Curate this topic Add this topic...
We tried to use the recently popular KAN convolution in UNet to further explore the possibility of KAN replacing traditional convolutional neural networks. In our preliminary experiments,we directly replaced the traditional convolution in UNet with KANConv without changing the network structure, including...