I tend to think of a matched filter as an "optimum denoiser" (in the mean squared error sense with white gaussian noise), but it's not an optimum detector in the general case, or an optimum "de-interferer".

The case shown here should be more resilient to changes in signal amplitude and non-matching interference.

Of course, because the kernel is actually learned, it can adapt to interference and try to be a "matched filter to what we want, plus anti-matched filter to interference" (i.e. give near-0 response to common interference signals). But SCS does seem like a more natural way to achieve a similarity metric and feature detection. (although this non-flexibility might work against it)

optimal among linear functions, and the noise is gaussian...

of course noise is not gaussian in many interesting applications and nothing is stopping a nonlinear function from getting better results than a matched filter, we just have no theory to discover what such a function would be

so yes, like you, i'd need to see results on actual data before spending time to learn more about it...

In my opinion, this looks promising:

https://github.com/stevenwalton/SCS-CCT

4% improvement over Compact Transformers, which is considered SOTA

EDIT: As meragrin_ pointed out, I read the graph the wrong way. It's actually 4% worse. Sorry.

Where do you see improvement? According to the graph and text, there is a 4% decrease in performance. It is slightly worse.