[D] Specific tips on Machine Learning research in a PhD
I am a new Machine Learning PhD and my topic is roughly vision, i.e. semantic/instance segmentation, and to be honest I am a little lost.
How exactly, specifically do you conduct research in this field? How does the day to day work look like?
- Do you think of new NN architectures and test them experimentally?
- Do you download others models and just try them out with own datasets?
- How do you keep track on different architectures, papers, etc. Maybe make an excel document with all the papers you’ve read with a short summary?
I would be really interested in how the day to day work of other researchers in the field looks like and what specific tips you might have.