KAN or MLP: A Fairer Comparison
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
66a06d8c8a65778f7264be7f
Runpeng Yu
claimed_verified
2024-07-24T07:25:24.009Z
635364b3c41f548fe39db945
/avatars/ad1916bbfabca0b6651c8eabacc5eba8.svg
Runpeng Yu
user
rp-yu
66a06d8c8a65778f7264be80
Weihao Yu
admin_assigned
2024-07-24T07:34:08.644Z
5df833bdda6d0311fd3d5403
https://cdn-avatars.huggingface.co/v1/production/uploads/5df833bdda6d0311fd3d5403/62OtGJEQXdOuhV9yCd4HS.png
Weihao Yu
user
whyu
66a06d8c8a65778f7264be81
Xinchao Wang
admin_assigned
2024-07-24T08:01:30.175Z
63fc03a50aab060792ffef39
/avatars/9d5b1bb2a41928e08176b703935133ab.svg
Wangxinchao
user
wxcTest
KAN or MLP: A Fairer Comparison
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
A comprehensive comparison of KAN and MLP models across diverse tasks reveals that MLP generally outperforms KAN except in symbolic formula representation where KAN's B-spline activation function provides advantage, and KAN exhibits more severe forgetting issues in class-incremental continual learning.
KAN MLP B-spline activation function class-incremental continual learning forgetting issue ablation studies parameter-efficient FLOPs