every blog every motto: You can do more than you think.
https://blog.csdn.net/weixin_39190382?type=blog

0. 前言

U-KAN来了，快是真的快的，上个月才出的KAN，不得不说快。

先占个坑，有时间细看。

下面放上摘要

1. 正文

下面是摘要

U-Net has become a cornerstone in various visual applications such as image
segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or
MLPs, the networks are still limited to linearly modeling patterns as well as the
deficient interpretability. To address these challenges, our intuition is inspired by
the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of
accuracy and interpretability, which reshape the neural network learning via the
stack of non-linear learnable activation functions derived from the KolmogorovAnold representation theorem. Specifically, in this paper, we explore the untapped
potential of KANs in improving backbones for vision tasks. We investigate, modify
and re-design the established U-Net pipeline by integrating the dedicated KAN
layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of U-KAN by higher
accuracy even with less computation cost. We further delved into the potential of
U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating
its applicability in generating task-oriented model architectures. These endeavours
unveil valuable insights and sheds light on the prospect that with U-KAN, you can
make strong backbone for medical image segmentation and generation