  This paper does not introduce a novel method. Instead, it offers a fairer and
more comprehensive comparison of KAN and MLP models across various tasks,
including machine learning, computer vision, audio processing, natural language
processing, and symbolic formula representation. Specifically, we control the
number of parameters and FLOPs to compare the performance of KAN and MLP. Our
main observation is that, except for symbolic formula representation tasks, MLP
generally outperforms KAN. We also conduct ablation studies on KAN and find
that its advantage in symbolic formula representation mainly stems from its
B-spline activation function. When B-spline is applied to MLP, performance in
symbolic formula representation significantly improves, surpassing or matching
that of KAN. However, in other tasks where MLP already excels over KAN,
B-spline does not substantially enhance MLP's performance. Furthermore, we find
that KAN's forgetting issue is more severe than that of MLP in a standard
class-incremental continual learning setting, which differs from the findings
reported in the KAN paper. We hope these results provide insights for future
research on KAN and other MLP alternatives. Project link:
https://github.com/yu-rp/KANbeFair
