One of my hobbies is reading about image processing. It is a love-hate relationship because I don't have a background in mathematics or programming, and as a non-scientist, I have limited access to papers, code and resources. In terms of papers, I really dislike the research (patent) paywall (red tape).
Anyway, here are some thoughts that come to mind after skimming through this thread:
Machine learning (ML) is certainly a hot topic nowadays. What I am wondering is: Does G'MIC have the capability to address machine learning? If not, is it feasible to extend it to the point where it can? Or is there another open source project that could better address the problem?
@iarga brings up a good point that shouldn't be missed.
The genius of ML is that it can be adapted for (m)any type of problem(s). It doesn't have to necessarily be about the transferring of artistic style. So, I think we can split the discussion into two problems: one about ML and the other about transferring artistic style. As @David_Tschumperle put it:
- As for yet-another-paper, a quick search yielded this one from IPOL: http://www.ipol.im/pub/art/2016/150/. I know it isn't exactly about artistic transfer and I don't have time to read it and play with the demo to see if it is on-topic, but we can always learn from how others approach a similar problem.
- When it comes to ML, we don't necessarily have to start from the beginning. There are many pre-trained databases out there that can be used with some caveats after some tweaking. It will be a compromise but a small community with limited resources probably couldn't match their scale and quality.