  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 Kolmogorov-Anold 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. Project page:
https://yes-ukan.github.io/
