Tweaking a CNN without retraining makes it sound like you want a no-code option on your machine learning.

Totally agree that model interpretability is a challenge, but there is a whole subsection of our field working on that. The fundamental design of deep learning sort of precludes what you’re talking about - at least given our current understanding of model interpretation. At best, a model may be trained to give options on certain aspects based on its input (we see this all the time), but that doesn’t sound like what you want. It sounds like you want to be able to target specific and arbitrary components of an output and intuitively modify the weights of all nodes contributing to that part of the output - presumably in isolation.

I think your challenge might lie with a fundamental lack of understanding of how these models actually work. I don’t mean that as a dig - they’re complicated. I just want to help bring you to a place of understanding about why the field is how you’re experiencing it.

Not a huge fan of massive edits to original posts after people have started responding. Your newly added recommendations put an onerous responsibility on any open source authors who might make their work public as a hobby rather than a career.