Some helpful gut checks:

1. Do you have reason to believe that your method will scale (with parameters and data)? Maybe (probably) you can't actually test things at Google scale--but if you have good theoretical reasons to believe that your method would be accretive at scale, that is a major +.

Yes, getting things to run really well at small scale can be of (sometimes extreme!) value--but you're simply going to see less interest from reviewers on its own. There have been a bazillion hacky ML methods that turn out to be entirely irrelevant once you scale up substantially, and people are wary of such papers/discussions.

If you've got to go down this path, then make sure to position it explicitly as hyper-optimizing small-scale models (like for mobile).)

2. Do you have good reasons to believe that the "top" paper plus your method would further boost SOTA? Even better, can you test it to confirm?

If your method is--at its theoretical core--simply a twist on a subset of the methods from that SOTA used, then you're going to see much less paper interest, unless you can promise significant improvements in simplicity/efficiency.

> But this "SOTA" paper uses some methods that just don't seem practical for applications at all.

3. Can you demonstrate the superiority of your method on some of these other applications? So that you can, e.g., create an SOTA in some sort of subset? That can be helpful.