This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations to approximate intricate functions. In this study, we integrate KAN with a ResNet-50 model pre-trained on ImageNet and evaluate its performance on the CIFAR-10 dataset. We perform a systematic hyperparameter search, focusing on grid size and spline degree (k), to optimize KAN's flexibility and accuracy. Our results demonstrate that KAN consistently outperforms traditional linear probing, achieving significant improvements in accuracy and generalization across a range of configurations. These findings indicate that KAN offers a more powerful and adaptable alternative to conventional linear probing techniques in transfer learning.

本研究针对传统线性探测方法在转移学习中无法建模复杂数据关系的局限性，提出了使用Kolmogorov-Arnold网络(KAN)作为替代方案。研究结果表明，KAN在多种配置下均显著提升了准确性和泛化能力，提供了比传统线性探测更强大和灵活的替代方案。

重新想象线性探测：转移学习中的Kolmogorov-Arnold网络