Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its convolutional variants in image classification and semantic segmentation. The focus is placed on examining their characteristics across varying data scales and noise levels. Results indicate that while KAN exhibits stronger fitting capabilities, it is highly sensitive to noise, limiting its robustness. To address this challenge, we propose a smoothness regularization method and introduce a Segment Deactivation technique. Both approaches enhance KAN's stability and generalization, demonstrating its potential in handling complex visual data tasks.

翻译：Kolmogorov-Arnold网络（KAN）作为一种理论高效的神经网络架构，因其捕获复杂模式的潜力而受到关注。然而，其在计算机视觉领域的应用仍相对未被探索。本研究首先分析了KAN在计算机视觉任务中的潜力，评估了KAN及其卷积变体在图像分类和语义分割中的性能。重点考察了它们在不同数据规模和噪声水平下的特性。结果表明，尽管KAN展现出更强的拟合能力，但其对噪声高度敏感，限制了其鲁棒性。为应对这一挑战，我们提出了一种平滑正则化方法，并引入了分段失活技术。这两种方法均提升了KAN的稳定性和泛化能力，证明了其在处理复杂视觉数据任务中的潜力。