It might depend a bit on your use case and how exactly the method is applied.
E.g. if your method would also speedup the inference (and even on e.g. the mobile platform), this might be a valid “deployment” mode (a lower model accuracy might be acceptable on mobile platforms).
For general training you would have to check how much work implementing your method would be, as even small accuracy drops might scare people from trying out your method. E.g. the quantization accuracy results show a smaller accuracy drop for a similar or higher performance gain.
But again, it might depend on the overall complexity of your method and in which situation you are working on it. E.g. if it’s a new research direction, which might be part of your studies (or PhD), small steps might be good enough to create a foundation and fix some issues in the next steps.