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:
this https URL

我们通过将 KAN 层整合到中间表示中，探索了 Kolmogorov-Arnold 网络（KANs）在改进视觉任务的骨干网络中的潜力，并证明了 U-KAN 在医学图像分割中的优越性。

U-KAN 为医学图像分割和生成提供了强大的骨干

U-KAN Makes Strong Backbone for Medical Image Segmentation and  Generation

The aim of this paper is to give an overview of domain adaptation and
transfer learning with a specific view on visual applications. After a general
motivation, we first position domain adaptation in the larger transfer learning
problem. Second, we try to address and analyze briefly the state-of-the-art
methods for different types of scenarios, first describing the historical
shallow methods, addressing both the homogeneous and the heterogeneous domain
adaptation methods. Third, we discuss the effect of the success of deep
convolutional architectures which led to new type of domain adaptation methods
that integrate the adaptation within the deep architecture. Fourth, we overview
the methods that go beyond image categorization, such as object detection or
image segmentation, video analyses or learning visual attributes. Finally, we
conclude the paper with a section where we relate domain adaptation to other
machine learning solutions.

本文介绍领域自适应和迁移学习的概述，涵盖不同类型的方案和历史浅层方法、深度卷积架构、超出图像分类的领域适应方法，以及将领域适应与其他机器学习解决方案相关联的讨论。