I think the real issue is that the OP has a mistaken expectation that they should understand everything. For instance, the group that wrote the Wasserstein GAN

I think the real issue is that the OP has a mistaken expectation that they should understand everything. For instance, the group that wrote the Wasserstein GAN paper are surely those that think night and day about distance metrics. And they might be totally lost reading a paper about some energy based method that relies on concepts from physics. The point is that researchers have their little niche and they try to make contributions in areas adjacent to it. It's unrealistic to think everyone publishing papers understand all the other papers, particularly in such a cross-disciplinary field like ML. There's also a big gap between a researcher deep in their career and a student fresh out of a masters program. It's also hard to transition from someone who's used to reading and understanding textbooks to someone who's often reading really technical research and understanding very little of it at first. You just have to push through and have confidence that you'll eventually learn enough to make a contribution. That's what it means to "become an expert"--you start off as not being an expert and then beat your head against the wall for a few years until you bootstrap your way out of it. And if you want to do it in a reasonable amount of time, you should probably choose something you have some of the fundamentals for.