Thanks for the link to Conv-KAN!. I had a quick look and I have a few points. Firstly, they use a different implementation compared to the original KAN paper, i.e. they use efficient-kan (
https://github.com/Blealtan/efficient-kan) - maybe some of the issues are negated in this alternative implementation? Secondly, it could be that there is something that's a bit different when applying KAN through convolution layers - again, something that negates some of the holes pinpointed in that post. Thirdly, Conv-KAN authors actually claim "At the moment we aren't seeing a significant improvement in the performance of the KAN Convolutional Networks compared to the traditional Convolutional Networks".